publications | Michael E. Papka

2025

Zhong Zheng, Seyfal Sultanov, Michael E. Papka, and Zhiling Lan, Exploring Uncore Frequency Scaling for Heterogeneous Computing, 2025
BibTeX
```
@misc{ZhSuPa25,
  author = {Zheng, Zhong and Sultanov, Seyfal and Papka, Michael E. and Lan, Zhiling},
  title = {Exploring Uncore Frequency Scaling for Heterogeneous Computing},
  year = {2025},
  archiveprefix = {arXiv},
  eprint = {2502.03796},
  primaryclass = {cs.DC},
}
```

Mathis Bode, Damian Alvarez, Paul Fischer, Christos E. Frouzakis, Jens Henrik Göbbert, Joseph A. Insley, Yu-Hsiang Lan, Victor A. Mateevitsi, Misun Min, Michael E. Papka, Silvio Rizzi, Roshan J. Samuel, and Jörg Schumacher, Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow, 2025

BibTeX

@misc{BoAlFi25,
  author = {Bode, Mathis and Alvarez, Damian and Fischer, Paul and Frouzakis, Christos E. and Göbbert, Jens Henrik and Insley, Joseph A. and Lan, Yu-Hsiang and Mateevitsi, Victor A. and Min, Misun and Papka, Michael E. and Rizzi, Silvio and Samuel, Roshan J. and Schumacher, Jörg},
  title = {Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow},
  year = {2025},
  archiveprefix = {arXiv},
  eprint = {2501.13240},
  primaryclass = {physics.flu-dyn},
}

Zhong Zheng, Michael E. Papka, and Zhiling Lan, More for Less: Integrating Capability-Predominant and Capacity-Predominant Computing, 2025
BibTeX
```
@misc{ZhPaLa25,
  author = {Zheng, Zhong and Papka, Michael E. and Lan, Zhiling},
  title = {More for Less: Integrating Capability-Predominant and Capacity-Predominant Computing},
  year = {2025},
  archiveprefix = {arXiv},
  eprint = {2501.12464},
  primaryclass = {cs.DC},
}
```

2024

Idunnuoluwa A. Adeniji, Joseph A. Insley, David Joiner, Victor A. Mateevitsi, Michael E. Papka, and Silvio Rizzi, Exploring Large-Scale Scientific Data in Virtual Reality, In 2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), 2024

BibTeX

@inproceedings{AdInJo24,
  title = {Exploring Large-Scale Scientific Data in Virtual Reality},
  author = {Adeniji, Idunnuoluwa A. and Insley, Joseph A. and Joiner, David and Mateevitsi, Victor A. and Papka, Michael E. and Rizzi, Silvio},
  year = {2024},
  booktitle = {2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)},
  pages = {75--76},
  doi = {10.1109/LDAV64567.2024.00019},
  keywords = {Three-dimensional displays;Pipelines;Data visualization;Focusing;Virtual reality;Games;Software;Hardware;Streams;Engines},
}

Gautham Dharuman, Kyle Hippe, Alexander Brace, Sam Foreman, Väinä Hatanpää, Varuni K. Sastry, Huihuo Zheng, Logan Ward, Servesh Muralidharan, Archit Vasan, Bharat Kale, Carla M. Mann, Heng Ma, Yun-Hsuan Cheng, Yuliana Zamora, Shengchao Liu, Chaowei Xiao, Murali Emani, Tom Gibbs, Mahidhar Tatineni, Deepak Canchi, Jerome Mitchell, Koichi Yamada, Maria Garzaran, Michael E. Papka, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, and Arvind Ramanathan, MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization, In 2024 SC24: International Conference for High Performance Computing, Networking, Storage and Analysis SC,Nov, 2024

Abstract BibTeX

We present a scalable, end-to-end workflow for protein design. By augmenting protein sequences with natural language descriptions of their biochemical properties, we train generative models that can be preferentially aligned with protein fitness landscapes. Through complex experimental- and simulationbased observations, we integrate these measures as preferred parameters for generating new protein variants and demonstrate our workflow on five diverse supercomputers. We achieve >1 ExaFLOPS sustained performance in mixed precision on each supercomputer and a maximum sustained performance of 4.11 ExaFLOPS and peak performance of 5.57 ExaFLOPS. We establish the scientific performance of our model on two tasks: (1) across a predetermined benchmark dataset of deep mutational scanning experiments to optimize the fitness-determining mutations in the yeast protein HIS7, and (2) in optimizing the design of the enzyme malate dehydrogenase to achieve lower activation barriers (and therefore increased catalytic rates) using simulation data. Our implementation thus sets high watermarks for multimodal protein design workflows.

@inproceedings{DhHiBr24,
  title = {{ MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization}},
  author = {Dharuman, Gautham and Hippe, Kyle and Brace, Alexander and Foreman, Sam and Hatanpää, Väinä and Sastry, Varuni K. and Zheng, Huihuo and Ward, Logan and Muralidharan, Servesh and Vasan, Archit and Kale, Bharat and Mann, Carla M. and Ma, Heng and Cheng, Yun-Hsuan and Zamora, Yuliana and Liu, Shengchao and Xiao, Chaowei and Emani, Murali and Gibbs, Tom and Tatineni, Mahidhar and Canchi, Deepak and Mitchell, Jerome and Yamada, Koichi and Garzaran, Maria and Papka, Michael E. and Foster, Ian and Stevens, Rick and Anandkumar, Anima and Vishwanath, Venkatram and Ramanathan, Arvind},
  year = {2024},
  month = nov,
  booktitle = {2024 SC24: International Conference for High Performance Computing, Networking, Storage and Analysis SC},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  pages = {74--86},
  doi = {10.1109/sc41406.2024.00013},
  url = {https://doi.IEEEcomputersociety.org/10.1109/SC41406.2024.00013},
  keywords = {HPC;AI;Large language models;protein design},
}

Colleen Heinemann, Jefferson Amstutz, Joseph A. Insley, Victor A. Mateevitsi, Michael E. Papka, and Silvio Rizzi, Graphical Representation Through a User Interface for In Situ Scientific Visualization with Ascent, In 2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), 2024

BibTeX

@inproceedings{HeJeIn24,
  title = {Graphical Representation Through a User Interface for In Situ Scientific Visualization with Ascent},
  author = {Heinemann, Colleen and Amstutz, Jefferson and Insley, Joseph A. and Mateevitsi, Victor A. and Papka, Michael E. and Rizzi, Silvio},
  year = {2024},
  booktitle = {2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)},
  pages = {71--72},
  doi = {10.1109/LDAV64567.2024.00017},
  keywords = {Data analysis;Pipelines;Data visualization;Organizations;Software;Graphical user interfaces;In situ visualization;graphical user interface (GUI);HPC simulations;scientific visualization;Ascent},
}

Andrew E. Johnson, Luc Renambot, G. Elisabeta Marai, Daria Tsoupikova, Michael E. Papka, Lance Long, Dana Plepys, Jonas Talandis, Maxine D. Brown, Jason Leigh, Daniel J. Sandin, and Thomas A. DeFanti, Electronic Visualization Laboratory’s 50th Anniversary Retrospective: Look to the Future, Build on the Past, PRESENCE: Virtual and Augmented Reality,Apr, 2024
Abstract BibTeX

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL’s introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL’s culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person’s expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL’s past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.
```
@article{JoReMa24,
  title = {{Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past}},
  author = {Johnson, Andrew E. and Renambot, Luc and Marai, G. Elisabeta and Tsoupikova, Daria and Papka, Michael E. and Long, Lance and Plepys, Dana and Talandis, Jonas and Brown, Maxine D. and Leigh, Jason and Sandin, Daniel J. and DeFanti, Thomas A.},
  year = {2024},
  month = apr,
  journal = {PRESENCE: Virtual and Augmented Reality},
  pages = {1--51},
  doi = {10.1162/pres_a_00421},
  issn = {1054-7460},
  url = {https://doi.org/10.1162/pres_a_00421},
}
```
Ratanond Koonchanok, Michael E. Papka, and Khairi Reda, Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations, IEEE Transactions on Visualization and Computer Graphics, 2024
Abstract BibTeX

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making.
```
@article{KoPaRe24,
  title = {Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations},
  author = {Koonchanok, Ratanond and Papka, Michael E. and Reda, Khairi},
  year = {2024},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  pages = {1--11},
  doi = {10.1109/tvcg.2024.3456182},
  keywords = {Bayes methods;Data visualization;Uncertainty;Correlation;Accuracy;Decision making;Biological system modeling;Visual inference;statistical rationality;human-machine collaboration},
}
```

Thomas Marrinan, Ethan Honzik, Hal L. N. Brynteson, and Michael E. Papka, Image Synthesis from a Collection of Depth Enhanced Panoramas: Creating Interactive Extended Reality Experiences from Static Images, In Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 2024

Abstract BibTeX

Stereoscopic 360° panoramas are a popular modality for creating cinematic virtual reality experiences. However, media in this format are typically static entities that consumers passively view. This is because objects visible in the scene, and camera properties such as depth of field, must be determined at capture time. We propose a real-time technique for dynamically synthesizing stereoscopic 360° panoramas from a collection of depth enhanced monoscopic panoramas. Using depth information, pixels can be transformed into three-dimensional space and re-projected to a different camera location. Our technique allows for head-motion parallax, dynamic depth of field, and integration of properly occluded virtual objects into the captured scene. Our technique shows minimal discrepancies compared to ground truth captures and reduces error when compared to existing stereoscopic 360° panoramic synthesis techniques. Additionally, our technique makes creating interactive extended reality experiences more accessible since monoscopic 360° cameras are much more common than their stereoscopic counterparts.

@inproceedings{MaHoBr24,
  title = {Image Synthesis from a Collection of Depth Enhanced Panoramas: Creating Interactive Extended Reality Experiences from Static Images},
  author = {Marrinan, Thomas and Honzik, Ethan and Brynteson, Hal L. N. and Papka, Michael E.},
  year = {2024},
  booktitle = {Proceedings of the 2024 ACM International Conference on Interactive Media Experiences},
  location = {Stockholm, Sweden},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Imx '24},
  pages = {64–74},
  doi = {10.1145/3639701.3656312},
  isbn = {9798400705038},
  url = {https://doi.org/10.1145/3639701.3656312},
  keywords = {360° panoramas, Cinematic virtual reality, Image-based rendering, Omni-directional stereo},
  numpages = {11},
}

Thomas Marrinan, Victor A. Mateevitsi, Madeleine Moeller, Alina Kanayinkal, and Michael E. Papka, 2023 IEEE Scientific Visualization Contest Winner: VisAnywhere: Developing Multiplatform Scientific Visualization Applications , IEEE Computer Graphics and Applications,Sep, 2024

Abstract BibTeX

Scientists often explore and analyze large-scale scientific simulation data by leveraging 2-D and 3-D visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 IEEE Scientific Visualization Contest, we present our work leveraging various web technologies to create a multiplatform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both colocated and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

@article{MaMaMo24,
  title = {{ 2023 IEEE Scientific Visualization Contest Winner: VisAnywhere: Developing Multiplatform Scientific Visualization Applications }},
  author = {Marrinan, Thomas and Mateevitsi, Victor A. and Moeller, Madeleine and Kanayinkal, Alina and Papka, Michael E.},
  year = {2024},
  month = sep,
  journal = {IEEE Computer Graphics and Applications},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  volume = {44},
  number = {05},
  pages = {93--103},
  doi = {10.1109/mcg.2024.3444460},
  issn = {1558-1756},
  url = {https://doi.IEEEcomputersociety.org/10.1109/MCG.2024.3444460},
  keywords = {Neurons;Data visualization;Collaboration;Three-dimensional displays;Calcium;Web design;Task analysis},
}

Victor A. Mateevitsi, Michael E. Papka, and Khairi Reda, Science in a Blink: Supporting Ensemble Perception in Scalar Fields , In 2024 IEEE Visualization and Visual Analytics (VIS),Oct, 2024
Abstract BibTeX

Visualizations support rapid analysis of scientific datasets, allowing viewers to glean aggregate information (e.g., the mean) within split-seconds. While prior research has explored this ability in conventional charts, it is unclear if spatial visualizations used by computational scientists afford a similar ensemble perception capacity. We investigate people’s ability to estimate two summary statistics, mean and variance, from pseudocolor scalar fields. In a crowd- sourced experiment, we find that participants can reliably characterize both statistics, although variance discrimination requires a much stronger signal. Multi-hue and diverging colormaps outperformed monochromatic, luminance ramps in aiding this extraction. Analysis of qualitative responses suggests that participants often estimate the distribution of hotspots and valleys as visual proxies for data statistics. These findings suggest that people’s summary interpretation of spatial datasets is likely driven by the appearance of discrete color segments, rather than assessments of overall luminance. Implicit color segmentation in quantitative displays could thus prove more useful than previously assumed by facilitating quick, gist- level judgments about color-coded visualizations.
```
@inproceedings{MaPaKh24,
  title = {{ Science in a Blink: Supporting Ensemble Perception in Scalar Fields }},
  author = {Mateevitsi, Victor A. and Papka, Michael E. and Reda, Khairi},
  year = {2024},
  month = oct,
  booktitle = {2024 IEEE Visualization and Visual Analytics (VIS)},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  pages = {216--220},
  doi = {10.1109/vis55277.2024.00051},
  url = {https://doi.IEEEcomputersociety.org/10.1109/VIS55277.2024.00051},
  keywords = {Image color analysis;Visual analytics;Aggregates;Data visualization;Spatial databases;Reliability},
}
```

Victor A. Mateevitsi, Andres Sewell, Mathis Bode, Paul Fischer, Jens Henrik Göbbert, Joseph A. Insley, Ioannis Kavroulakis, Damaskinos Konioris, Yu-Hsiang Lan, Misun Min, Dimitrios Papageorgiou, Michael E. Papka, Steve Petruzza, Silvio Rizzi, and Ananias Tomboulides, Visuals on the House: Optimizing HPC Workflows with No-Cost CPU Visualization, In 2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), 2024

BibTeX

@inproceedings{MaSeBo24,
  title = {Visuals on the House: Optimizing HPC Workflows with No-Cost CPU Visualization},
  author = {Mateevitsi, Victor A. and Sewell, Andres and Bode, Mathis and Fischer, Paul and Göbbert, Jens Henrik and Insley, Joseph A. and Kavroulakis, Ioannis and Konioris, Damaskinos and Lan, Yu-Hsiang and Min, Misun and Papageorgiou, Dimitrios and Papka, Michael E. and Petruzza, Steve and Rizzi, Silvio and Tomboulides, Ananias},
  year = {2024},
  booktitle = {2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)},
  pages = {69--70},
  doi = {10.1109/LDAV64567.2024.00016},
  keywords = {Performance evaluation;Visualization;Leadership;Ion radiation effects;High performance computing;Data visualization;Propulsion;Rendering (computer graphics);Resource management;Magnetosphere;High Performance Computing (HPC);In Situ Visualization;Computational Fluid Dynamics (CFD);GPU Computing;Parallel Processing},
}

Xiaolong Ma, Feng Yan, Lei Yang, Ian Foster, Michael E. Papka, Zhengchun Liu, and Rajkumar Kettimuthu, MalleTrain: Deep Neural Networks Training on Unfillable Supercomputer Nodes, In Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering, 2024
Abstract BibTeX

First-come first-serve scheduling can result in substantial (up to 10%) of transiently idle nodes on supercomputers. Recognizing that such unfilled nodes are well-suited for deep neural network (DNN) training, due to the flexible nature of DNN training tasks, Liu et al. proposed that the re-scaling DNN training tasks to fit gaps in schedules be formulated as a mixed-integer linear programming (MILP) problem, and demonstrated via simulation the potential benefits of the approach. Here, we introduce MalleTrain, a system that provides the first practical implementation of this approach and that furthermore generalizes it by allowing it to be used even for DNN training applications for which model information is unknown before runtime. Key to this latter innovation is the use of a lightweight online job profiling advisor (JPA) to collect critical scalability information for DNN jobs—information that it then employs to optimize resource allocations dynamically, in real time. We describe the MalleTrain architecture and present the results of a detailed experimental evaluation on a supercomputer GPU cluster and several representative DNN training workloads, including neural architecture search and hyperparameter optimization. Our results not only confirm the practical feasibility of leveraging idle supercomputer nodes for DNN training but improve significantly on prior results, improving training throughput by up to 22.3% without requiring users to provide job scalability information.
```
@inproceedings{MaYaYa24,
  title = {MalleTrain: Deep Neural Networks Training on Unfillable Supercomputer Nodes},
  author = {Ma, Xiaolong and Yan, Feng and Yang, Lei and Foster, Ian and Papka, Michael E. and Liu, Zhengchun and Kettimuthu, Rajkumar},
  year = {2024},
  booktitle = {Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering},
  location = {London, United Kingdom},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Icpe '24},
  pages = {190–200},
  doi = {10.1145/3629526.3645035},
  isbn = {9798400704444},
  url = {https://doi.org/10.1145/3629526.3645035},
  keywords = {deep neural network, distributed deep learning training, resource management, scheduling, supercomputer},
  numpages = {11},
}
```
Amey Salvi, Kecheng Lu, Michael E. Papka, Yunhai Wang, and Khairi Reda, Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps, In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024
Abstract BibTeX

Quantitative data is frequently represented using color, yet designing effective color mappings is a challenging task, requiring one to balance perceptual standards with personal color preference. Current design tools either overwhelm novices with complexity or offer limited customization options. We present ColorMaker, a mixed-initiative approach for creating colormaps. ColorMaker combines fluid user interaction with real-time optimization to generate smooth, continuous color ramps. Users specify their loose color preferences while leaving the algorithm to generate precise color sequences, meeting both designer needs and established guidelines. ColorMaker can create new colormaps, including designs accessible for people with color-vision deficiencies, starting from scratch or with only partial input, thus supporting ideation and iterative refinement. We show that our approach can generate designs with similar or superior perceptual characteristics to standard colormaps. A user study demonstrates how designers of varying skill levels can use this tool to create custom, high-quality colormaps. ColorMaker is available at: colormaker.org
```
@inproceedings{SaLuPa24,
  title = {Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps},
  author = {Salvi, Amey and Lu, Kecheng and Papka, Michael E. and Wang, Yunhai and Reda, Khairi},
  year = {2024},
  booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},
  location = {Honolulu, HI, USA},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Chi '24},
  doi = {10.1145/3613904.3642265},
  isbn = {9798400703300},
  url = {https://doi.org/10.1145/3613904.3642265},
  articleno = {145},
  keywords = {Mixed-initiative systems, color design, colormaps, simulated annealing.},
  numpages = {17},
}
```

Andres Sewell, Landon Dyken, Victor A. Mateevitsi, Will Usher, Jefferson Amstutz, Thomas Marrinan, Khairi Reda, Silvio Rizzi, Joseph A. Insley, Michael E. Papka, Sidharth Kumar, and Steve Petruzza, High-quality Approximation of Scientific Data using 3D Gaussian Splatting, In 2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), 2024

BibTeX

@inproceedings{SeDyMa24,
  title = {High-quality Approximation of Scientific Data using 3D Gaussian Splatting},
  author = {Sewell, Andres and Dyken, Landon and Mateevitsi, Victor A. and Usher, Will and Amstutz, Jefferson and Marrinan, Thomas and Reda, Khairi and Rizzi, Silvio and Insley, Joseph A. and Papka, Michael E. and Kumar, Sidharth and Petruzza, Steve},
  year = {2024},
  booktitle = {2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)},
  pages = {73--74},
  doi = {10.1109/LDAV64567.2024.00018},
  keywords = {Point cloud compression;Solid modeling;Technological innovation;Three-dimensional displays;Pipelines;Rendering (computer graphics);Data models;Real-time systems;Image reconstruction;Isosurfaces;3D Gaussian splatting;scientific data;data reconstruction;machine learning},
}

Andres Sewell, Dimitrios K Fytanidis, Victor A Mateevitsi, Cyrus Harrison, Nicole Marsaglia, Thomas Marrinan, Silvio Rizzi, Joseph A Insley, Michael E Papka, and Steve Petruzza, Bridging Gaps in Simulation Analysis through a General Purpose, Bidirectional Steering Interface with Ascent, In ISAV 2024: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization,Nov, 2024

BibTeX

@inproceedings{SeFyDi24,
  title = {{Bridging Gaps in Simulation Analysis through a General Purpose, Bidirectional Steering Interface with Ascent}},
  author = {Sewell, Andres and Fytanidis, Dimitrios K and Mateevitsi, Victor A and Harrison, Cyrus and Marsaglia, Nicole and Marrinan, Thomas and Rizzi, Silvio and Insley, Joseph A and Papka, Michael E and Petruzza, Steve},
  year = {2024},
  month = nov,
  booktitle = {ISAV 2024: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
}

Shilpika Shilpika, Bethany Lusch, Murali Emani, Filippo Simini, Venkatram Vishwanath, Michael E. Papka, and Kwan-Liu Ma, A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems , In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid),May, 2024
Abstract BibTeX

The ability to monitor and interpret hardware system events and behaviors is crucial to improving the robustness and reliability of these systems, especially in a supercomputing facility. The growing complexity and scale of these systems demand an increase in monitoring data collected at multiple fidelity levels and varying temporal resolutions. In this work, we aim to build a holistic analytical system that helps make sense of such massive data, mainly the hardware logs, job logs, and environment logs collected from disparate subsystems and components of a supercomputer system. This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions. We use multi-resolution dynamic mode decomposition (mrDMD), a technique that depicts high-dimensional data as correlated spatial-temporal variations patterns or modes, to extract variation patterns isolated at specified frequencies. Our improvements to the mrDMD algorithm help promptly reveal useful information in the massive environment log dataset, which is then associated with the processed hardware and job log datasets using our visual analytics system. Furthermore, our system can identify the usage and error patterns filtered at user, project, and subcomponent levels. We exemplify the effectiveness of our approach with two use scenarios with the Cray XC40 supercomputer.
```
@inproceedings{ShLuEm24,
  title = {{ A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems }},
  author = {Shilpika, Shilpika and Lusch, Bethany and Emani, Murali and Simini, Filippo and Vishwanath, Venkatram and Papka, Michael E. and Ma, Kwan-Liu},
  year = {2024},
  month = may,
  booktitle = {2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  pages = {478--488},
  doi = {10.1109/CCGrid59990.2024.00060},
  url = {https://doi.IEEEcomputersociety.org/10.1109/CCGrid59990.2024.00060},
  keywords = {Visual analytics;Heuristic algorithms;Time series analysis;Supercomputers;Hardware;Robustness;Real-time systems;Spatial resolution;Monitoring;Multiresolution analysis},
}
```

Shilpika Shilpika, Bethany Lusch, Venkatram Vishwanath, and Michael E. Papka, An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems, In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2024

BibTeX

@inproceedings{ShLuVi24,
  author = {Shilpika, Shilpika and Lusch, Bethany and Vishwanath, Venkatram and Papka, Michael E.},
  title = {An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems},
  booktitle = {SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2024},
  pages = {1576--1587},
  doi = {10.1109/scw63240.2024.00197},
  keywords = {Heuristic algorithms;Visual analytics;Layout;Time series analysis;Data visualization;Supercomputers;Hardware;Sensors;Spatial resolution;Monitoring;high performance computing;scalability;visualization;incremental analysis;online analysis;mrDMD;Dynamic mode decomposition},
}

Qi Wu, Joseph A. Insley, Victor A. Mateevitsi, Silvio Rizzi, Michael E. Papka, and Kwan-Liu Ma, Distributed Neural Representation for Reactive In Situ Visualization, IEEE Transactions on Visualization and Computer Graphics, 2024
Abstract BibTeX

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.
```
@article{WuInMa24,
  title = {Distributed Neural Representation for Reactive In Situ Visualization},
  author = {Wu, Qi and Insley, Joseph A. and Mateevitsi, Victor A. and Rizzi, Silvio and Papka, Michael E. and Ma, Kwan-Liu},
  year = {2024},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  pages = {1--15},
  doi = {10.1109/tvcg.2024.3432710},
  keywords = {Data visualization;Data models;Computational modeling;Training;Adaptation models;Neural networks;Programming;Implicit neural representation;scientific visualization;in situ visualization;reactive programming},
}
```

2023

Hal Brynteson, Abbas Moradi Bilondi, Nicolo Scapin, Luca Brandt, Parisa Mirbod, and Michael E. Papka, Effects of employing liquid-liquid emulsions on heat transfer within a turbulent Rayleigh-Benard convection, In 76th Annual Meeting of the APS Division of Fluid Dynamics, 2023

BibTeX Poster

@inproceedings{BiBrMi23,
  author = {Brynteson, Hal and Bilondi, Abbas Moradi and Scapin, Nicolo and Brandt, Luca and Mirbod, Parisa and Papka, Michael E.},
  title = {Effects of employing liquid-liquid emulsions on heat transfer within a turbulent Rayleigh-Benard convection},
  booktitle = {76th Annual Meeting of the APS Division of Fluid Dynamics},
  year = {2023},
  date-added = {2024-10-01 13:42:02 -0500},
  date-modified = {2024-10-01 13:46:30 -0500},
  doi = {https://doi.org/10.1103/APS.DFD.2023.GFM.P0060},
}

Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, Defne G. Ozgulbas, Natalia Vassilieva, James Gregory Pauloski, Logan Ward, Valerie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, and Arvind Ramanathan, GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics, The International Journal of High Performance Computing Applications, 2023

Abstract BibTeX

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole-genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

@article{ZvBrHi23,
  author = {Zvyagin, Maxim and Brace, Alexander and Hippe, Kyle and Deng, Yuntian and Zhang, Bin and Bohorquez, Cindy Orozco and Clyde, Austin and Kale, Bharat and Perez-Rivera, Danilo and Ma, Heng and Mann, Carla M. and Irvin, Michael and Ozgulbas, Defne G. and Vassilieva, Natalia and Pauloski, James Gregory and Ward, Logan and Hayot-Sasson, Valerie and Emani, Murali and Foreman, Sam and Xie, Zhen and Lin, Diangen and Shukla, Maulik and Nie, Weili and Romero, Josh and Dallago, Christian and Vahdat, Arash and Xiao, Chaowei and Gibbs, Thomas and Foster, Ian and Davis, James J. and Papka, Michael E. and Brettin, Thomas and Stevens, Rick and Anandkumar, Anima and Vishwanath, Venkatram and Ramanathan, Arvind},
  title = {GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics},
  journal = {The International Journal of High Performance Computing Applications},
  year = {2023},
  volume = {37},
  number = {6},
  pages = {683--705},
  doi = {10.1177/10943420231201154},
  eprint = {https://doi.org/10.1177/10943420231201154},
  url = {https://doi.org/10.1177/10943420231201154},
}

Boyang Li, Zhiling Lan, and Michael E. Papka, Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling, In 2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2023
Abstract BibTeX

In the field of high-performance computing (HPC), there has been recent exploration into the use of deep reinforcement learning for cluster scheduling (DRL scheduling), which has demonstrated promising outcomes. However, a significant challenge arises from the lack of interpretability in deep neural networks (DNN), rendering them as black-box models to system managers. This lack of model interpretability hinders the practical deployment of DRL scheduling. In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling. The core idea is to interpret DNN (i.e., the DRL policy) as a decision tree by utilizing imitation learning. Unlike DNN, decision tree models are non-parametric and easily comprehensible to humans. To extract an effective and efficient decision tree, IRL incorporates the Dataset Aggregation (DAgger) algorithm and introduces the notion of critical state to prune the derived decision tree. Through trace-based experiments, we demonstrate that IRL is capable of converting a black-box DNN policy into an interpretable rule-based decision tree while maintaining comparable scheduling performance. Additionally, IRL can contribute to the setting of rewards in DRL scheduling.
```
@inproceedings{LiLaPa23,
  author = {Li, Boyang and Lan, Zhiling and Papka, Michael E.},
  title = {Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling},
  booktitle = {2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)},
  year = {2023},
  pages = {1--8},
  doi = {10.1109/mascots59514.2023.10387651},
  keywords = {Deep learning;Processor scheduling;Computational modeling;Closed box;Optimization methods;Reinforcement learning;Artificial neural networks;cluster scheduling;deep reinforcement learning;high-performance computing;interpretation;decision tree},
}
```
Victor A. Mateevitsi, Mathis Bode, Nicola Ferrier, Paul Fischer, Jens Henrik Göbbert, Joseph A. Insley, Yu-Hsiang Lan, Misun Min, Michael E. Papka, Saumil Patel, Silvio Rizzi, and Jonathan Windgassen, Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS Using SENSEI, In Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, 2023
Abstract BibTeX

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data "snapshots" to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use-cases and report on runs performed on Polaris, Argonne Leadership Computing Facility’s (ALCF) 44 Petaflop supercomputer and Jülich Wizard for European Leadership Science (JUWELS) Booster, Jülich Supercomputing Centre’s (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology.
```
@inproceedings{MaBoFe23,
  author = {Mateevitsi, Victor A. and Bode, Mathis and Ferrier, Nicola and Fischer, Paul and G\"{o}bbert, Jens Henrik and Insley, Joseph A. and Lan, Yu-Hsiang and Min, Misun and Papka, Michael E. and Patel, Saumil and Rizzi, Silvio and Windgassen, Jonathan},
  title = {Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS Using SENSEI},
  booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
  year = {2023},
  pages = {862–867},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Sc-w '23},
  doi = {10.1145/3624062.3624159},
  isbn = {9798400707858},
  location = {Denver, CO, USA},
  numpages = {6},
  url = {https://doi.org/10.1145/3624062.3624159},
}
```
Murali Emani, Sam Foreman, Varuni Sastry, Zhen Xie, Siddhisanket Raskar, William Arnold, Rajeev Thakur, Venkatram Vishwanath, and Michael E. Papka, A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators, 2023
Abstract arXiv BibTeX

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accuracy of the applications is contingent upon their efficient execution on the underlying hardware infrastructure. Specialized AI accelerator hardware systems have recently become available for accelerating AI applications. However, the comparative performance of these AI accelerators on large language models has not been previously studied. In this paper, we systematically study LLMs on multiple AI accelerators and GPUs and evaluate their performance characteristics for these models. We evaluate these systems with (i) a micro-benchmark using a core transformer block, (ii) a GPT- 2 model, and (iii) an LLM-driven science use case, GenSLM. We present our findings and analyses of the models’ performance to better understand the intrinsic capabilities of AI accelerators. Furthermore, our analysis takes into account key factors such as sequence lengths, scaling behavior, sparsity, and sensitivity to gradient accumulation steps.
```
@misc{EmFoSa23,
  author = {Emani, Murali and Foreman, Sam and Sastry, Varuni and Xie, Zhen and Raskar, Siddhisanket and Arnold, William and Thakur, Rajeev and Vishwanath, Venkatram and Papka, Michael E.},
  title = {A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators},
  year = {2023},
  archiveprefix = {arXiv},
  eprint = {2310.04607},
  primaryclass = {cs.PF},
}
```

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, and Rick Stevens, DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies, 2023

Abstract arXiv BibTeX

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today’s biggest science mysteries. By leveraging DeepSpeed’s current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.

@misc{SoKrZh23,
  author = {Song, Shuaiwen Leon and Kruft, Bonnie and Zhang, Minjia and Li, Conglong and Chen, Shiyang and Zhang, Chengming and Tanaka, Masahiro and Wu, Xiaoxia and Rasley, Jeff and Awan, Ammar Ahmad and Holmes, Connor and Cai, Martin and Ghanem, Adam and Zhou, Zhongzhu and He, Yuxiong and Luferenko, Pete and Kumar, Divya and Weyn, Jonathan and Zhang, Ruixiong and Klocek, Sylwester and Vragov, Volodymyr and AlQuraishi, Mohammed and Ahdritz, Gustaf and Floristean, Christina and Negri, Cristina and Kotamarthi, Rao and Vishwanath, Venkatram and Ramanathan, Arvind and Foreman, Sam and Hippe, Kyle and Arcomano, Troy and Maulik, Romit and Zvyagin, Maxim and Brace, Alexander and Zhang, Bin and Bohorquez, Cindy Orozco and Clyde, Austin and Kale, Bharat and Perez-Rivera, Danilo and Ma, Heng and Mann, Carla M. and Irvin, Michael and Pauloski, J. Gregory and Ward, Logan and Hayot, Valerie and Emani, Murali and Xie, Zhen and Lin, Diangen and Shukla, Maulik and Foster, Ian and Davis, James J. and Papka, Michael E. and Brettin, Thomas and Balaprakash, Prasanna and Tourassi, Gina and Gounley, John and Hanson, Heidi and Potok, Thomas E and Pasini, Massimiliano Lupo and Evans, Kate and Lu, Dan and Lunga, Dalton and Yin, Junqi and Dash, Sajal and Wang, Feiyi and Shankar, Mallikarjun and Lyngaas, Isaac and Wang, Xiao and Cong, Guojing and Zhang, Pei and Fan, Ming and Liu, Siyan and Hoisie, Adolfy and Yoo, Shinjae and Ren, Yihui and Tang, William and Felker, Kyle and Svyatkovskiy, Alexey and Liu, Hang and Aji, Ashwin and Dalton, Angela and Schulte, Michael and Schulz, Karl and Deng, Yuntian and Nie, Weili and Romero, Josh and Dallago, Christian and Vahdat, Arash and Xiao, Chaowei and Gibbs, Thomas and Anandkumar, Anima and Stevens, Rick},
  title = {DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
  year = {2023},
  archiveprefix = {arXiv},
  eprint = {2310.04610},
  primaryclass = {cs.AI},
}

Nickolaus Saint, Ryan Chard, Rafael Vescovi, Jim Pruyne, Ben Blaiszik, Rachana Ananthakrishnan, Michael E. Papka, Rick Wagner, Kyle Chard, and Ian Foster, Active Research Data Management with the Django Globus Portal Framework, In Practice and Experience in Advanced Research Computing, 2023
Abstract BibTeX

Publishing and sharing data is critical to fostering collaboration and advancing scientific research. Data portals are commonly used to organize, publish, and securely disseminate data—a critical step toward making data findable, accessible, interoperable, and reusable (FAIR). However, the diversity of scientific data types, sizes, and their location present significant challenges, e.g., it is difficult for portals to accommodate heterogenous research products when using strict metadata schemas and rigid interfaces. Thus, there is a need for a user-customizable data portal solution that enables rapid creation of new portals that may be tailored to a researchers needs while accommodating distributed data sources and engaging advanced computing resources. In this paper, we present the Django Globus Portal Framework (DGPF), a tool designed to help users rapidly create secure, customizable, and extensible data portals. DGPF is a powerful and flexible framework that builds upon the Globus platform for authentication, data sharing, creation of automation flows, and search capabilities, allowing for seamless integration with existing research workflows. We present the design and implementation of the DGPF and describe our experiences operating the Argonne Community Data Co-op (ACDC)—a collection of DGPF portals with over 1 M records and over 100 TB of published data that has been accessed by more than 300 users.
```
@inproceedings{SaChVe23,
  author = {Saint, Nickolaus and Chard, Ryan and Vescovi, Rafael and Pruyne, Jim and Blaiszik, Ben and Ananthakrishnan, Rachana and Papka, Michael E. and Wagner, Rick and Chard, Kyle and Foster, Ian},
  title = {Active Research Data Management with the Django Globus Portal Framework},
  booktitle = {Practice and Experience in Advanced Research Computing},
  year = {2023},
  pages = {43–51},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Pearc '23},
  doi = {10.1145/3569951.3593597},
  isbn = {9781450399852},
  keywords = {FAIR Data, Modern Research Data Portal, Globus},
  location = {Portland, OR, USA},
  numpages = {9},
  url = {https://doi.org/10.1145/3569951.3593597},
}
```
Zhengchun Liu, Rajkumar Kettimuthu, Michael E. Papka, and Ian Foster, FreeTrain: A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks, In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2023
Abstract BibTeX

Supercomputer scheduling policies commonly result in many transient idle nodes, a phenomenon that is only partially alleviated by backfill scheduling methods that promote small jobs to run before large jobs. Here we describe how to realize a novel use for these otherwise wasted resources, namely, deep neural network (DNN) training. This important workload is easily organized as many small fragments that can be configured dynamically to fit essentially any node × time hole in a supercomputer’s schedule. We describe how the task of rescaling suitable DNN training tasks to fit dynamically changing holes can be formulated as a deterministic mixed integer linear programming (MILP)-based resource allocation algorithm, and show that this MILP problem can be solved efficiently at run time. We show further how this MILP problem can be adapted to optimize for administrator- or user-defined metrics. We validate our method with supercomputer scheduler logs and different DNN training scenarios, and demonstrate efficiencies of up to 93% compared with running the same training tasks on dedicated nodes. Our method thus enables substantial supercomputer resources to be allocated to DNN training with no impact on other applications.
```
@inproceedings{LiKePa23,
  author = {Liu, Zhengchun and Kettimuthu, Rajkumar and Papka, Michael E. and Foster, Ian},
  title = {FreeTrain: A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks},
  booktitle = {2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)},
  year = {2023},
  pages = {299--310},
  doi = {doi.org/10.1109/CCGrid57682.2023.00036},
}
```
Boyang Li, Yuping Fan, Michael E. Papka, and Zhiling Lan, Encoding for Reinforcement Learning Driven Scheduling, In Job Scheduling Strategies for Parallel Processing, 2023
Abstract BibTeX

Reinforcement learning (RL) is exploited for cluster scheduling in the field of high-performance computing (HPC). One of the key challenges for RL driven scheduling is state representation for RL agent (i.e., capturing essential features of dynamic scheduling environment for decision making). Existing state encoding approaches either lack critical scheduling information or suffer from poor scalability. In this study, we present SEM (Scalable and Efficient encoding Model) for general RL driven scheduling in HPC. It captures system resource and waiting job state, both being critical information for scheduling. It encodes these pieces of information into a fixed-sized vector as an input to the agent. A typical agent is built on deep neural network, and its training/inference cost grows exponentially with the size of its input. Production HPC systems contain a large number of computer nodes. As such, a direct encoding of each of the system resources would lead to poor scalability of the RL agent. SEM uses two techniques to transform the system resource state into a small-sized vector, hence being capable of representing a large number of system resources in a vector of 100–200. Our trace-based simulations demonstrate that compared to the existing state encoding methods, SEM can achieve 9X training speedup and 6X inference speedup while maintaining comparable scheduling performance.
```
@inproceedings{LiFaPa23,
  author = {Li, Boyang and Fan, Yuping and Papka, Michael E. and Lan, Zhiling},
  editor = {Klus{\'a}{\v{c}}ek, Dalibor and Julita, Corbal{\'a}n and Rodrigo, Gonzalo P.},
  title = {Encoding for Reinforcement Learning Driven Scheduling},
  booktitle = {Job Scheduling Strategies for Parallel Processing},
  year = {2023},
  pages = {68--87},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  isbn = {978-3-031-22698-4},
}
```
Bharat Kale, Maoyuan Sun, and Michael E. Papka, The State of the Art in Visualizing Dynamic Multivariate Networks, Computer Graphics Forum, 2023
Abstract BibTeX

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.
```
@article{KaSuPa23,
  author = {Kale, Bharat and Sun, Maoyuan and Papka, Michael E.},
  title = {The State of the Art in Visualizing Dynamic Multivariate Networks},
  journal = {Computer Graphics Forum},
  year = {2023},
  volume = {42},
  number = {3},
  pages = {471--490},
  doi = {https://doi.org/10.1111/cgf.14856},
  keywords = {CCS Concepts, • Human-centered computing —> Graph drawings},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14856},
}
```
Bharat Kale, Austin Clyde, Maoyuan Sun, Arvind Ramanathan, Rick Stevens, and Michael E. Papka, ChemoGraph: Interactive Visual Exploration of the Chemical Space, Computer Graphics Forum, 2023
Abstract BibTeX

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
```
@article{KaClSu23,
  author = {Kale, Bharat and Clyde, Austin and Sun, Maoyuan and Ramanathan, Arvind and Stevens, Rick and Papka, Michael E.},
  title = {ChemoGraph: Interactive Visual Exploration of the Chemical Space},
  journal = {Computer Graphics Forum},
  year = {2023},
  volume = {42},
  number = {3},
  pages = {13--24},
  doi = {https://doi.org/10.1111/cgf.14807},
  keywords = {chemical space exploration, cheminformatics, multipartite graphs, data visualization, CCS Concepts, • Applied computing → Chemistry},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14807},
}
```

Jarrad Hampton-Marcell, Tasia Bryson, Jeffrey Larson, J. Taylor Childers, Spencer Pasero, Cortez Watkins, Thomas Reed, Dorletta Flucas-Payton, and Michael E. Papka, Leveraging national laboratories to increase Black representation in STEM: recommendations within the Department of Energy, International Journal of STEM Education, 2023

Abstract BibTeX

Increasing diversity in STEM disciplines has been a goal at scientific institutions for many decades. Black representation in STEM, however, has remained critically low at all levels (high school, undergraduate, graduate, and professional) for over 40 years, highlighting the need for innovative strategies that promote and retain Black students and professionals in STEM. We refocus efforts on increasing Black representation in STEM by promoting early exposure and continued engagement while leveraging national laboratories—an underutilized resource with immense potential to centralize diversity and inclusion efforts nationally.

@article{HaBrLa23,
  author = {Hampton-Marcell, Jarrad and Bryson, Tasia and Larson, Jeffrey and Childers, J. Taylor and Pasero, Spencer and Watkins, Cortez and Reed, Thomas and Flucas-Payton, Dorletta and Papka, Michael E.},
  title = {Leveraging national laboratories to increase Black representation in STEM: recommendations within the Department of Energy},
  journal = {International Journal of STEM Education},
  year = {2023},
  volume = {10},
  number = {1},
  pages = {4},
  date-added = {2023-03-19 08:49:30 -0500},
  date-modified = {2023-03-19 08:49:30 -0500},
  doi = {10.1186/s40594-022-00394-4},
  isbn = {2196-7822},
  url = {https://doi.org/10.1186/s40594-022-00394-4},
}

2022

Charlie Catlett, Pete Beckman, Nicola Ferrier, Michael E. Papka, Rajesh Sankaran, Jeff Solin, Valerie Taylor, Douglas Pancoast, and Daniel Reed, Hands-On Computer Science: The Array of Things Experimental Urban Instrument, Computing in Science & Engineering, 2022
Abstract BibTeX Code Website

Chicago’s Array of Things (AoT) project is aptly described as a technology experiment or a “smart city” prototype. The concept of such an extensible “instrument” arose within a larger translational research vision applying computer science and engineering research for the multidimensional benefit of people and communities in cities. The AoT project hypothesized that wireless intelligent sensor networks could enable both quantitative social science and urban monitoring while also stimulating youth interest in science and technology. Successful deployment of such sensor networks could provide open data from urban measurements not only in support of diverse research questions—in environmental dynamics, urban architecture, engineering, and social sciences—but also informing community groups and city planners. The AoT project and its successor SAGE project are a computer science and engineering experiment, but its success is inextricably tied to community engagement and experiential education. Simply put, community acceptance is a prerequisite to installing and testing the instrument.
```
@article{CaBeFe22,
  author = {Catlett, Charlie and Beckman, Pete and Ferrier, Nicola and Papka, Michael E. and Sankaran, Rajesh and Solin, Jeff and Taylor, Valerie and Pancoast, Douglas and Reed, Daniel},
  title = {Hands-On Computer Science: The Array of Things Experimental Urban Instrument},
  journal = {Computing in Science & Engineering},
  year = {2022},
  volume = {24},
  number = {1},
  pages = {57--63},
  doi = {10.1109/mcse.2021.3139405},
}
```

Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, Gregory J. Pauloski, Logan Ward, Valerie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, and Arvind Ramanathan, GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics, bioRxiv, 2022

Abstract BibTeX

We seek to transform how new and emergent variants of pandemiccausing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million prokaryotic gene sequences and finetuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.Competing Interest StatementThe authors have declared no competing interest.

@article{ZvBrHi22,
  author = {Zvyagin, Maxim and Brace, Alexander and Hippe, Kyle and Deng, Yuntian and Zhang, Bin and Bohorquez, Cindy Orozco and Clyde, Austin and Kale, Bharat and Perez-Rivera, Danilo and Ma, Heng and Mann, Carla M. and Irvin, Michael and Pauloski, Gregory J. and Ward, Logan and Hayot-Sasson, Valerie and Emani, Murali and Foreman, Sam and Xie, Zhen and Lin, Diangen and Shukla, Maulik and Nie, Weili and Romero, Josh and Dallago, Christian and Vahdat, Arash and Xiao, Chaowei and Gibbs, Thomas and Foster, Ian and Davis, James J. and Papka, Michael E. and Brettin, Thomas and Stevens, Rick and Anandkumar, Anima and Vishwanath, Venkatram and Ramanathan, Arvind},
  title = {GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics},
  journal = {bioRxiv},
  year = {2022},
  publisher = {Cold Spring Harbor Laboratory},
  doi = {10.1101/2022.10.10.511571},
  url = {https://www.biorxiv.org/content/early/2022/11/23/2022.10.10.511571},
}

Rafael Vescovi, Ryan Chard, Nickolaus D. Saint, Ben Blaiszik, Jim Pruyne, Tekin Bicer, Alex Lavens, Zhengchun Liu, Michael E. Papka, Suresh Narayanan, Nicholas Schwarz, Kyle Chard, and Ian T. Foster, Linking scientific instruments and computation: Patterns, technologies, and experiences, Patterns, 2022

Abstract BibTeX

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets of such massive data streams, such as by explicitly discarding some data elements or by directing instruments to relevant areas of experimental space. Thus, methods are required for configuring and running distributed computing pipelines—what we call flows—that link instruments, computers (e.g., for analysis, simulation, artificial intelligence [AI] model training), edge computing (e.g., for analysis), data stores, metadata catalogs, and high-speed networks. We review common patterns associated with such flows and describe methods for instantiating these patterns. We present experiences with the application of these methods to the processing of data from five different scientific instruments, each of which engages powerful computers for data inversion,model training, or other purposes. We also discuss implications of such methods for operators and users of scientific facilities.

@article{VeChSa22,
  author = {Vescovi, Rafael and Chard, Ryan and Saint, Nickolaus D. and Blaiszik, Ben and Pruyne, Jim and Bicer, Tekin and Lavens, Alex and Liu, Zhengchun and Papka, Michael E. and Narayanan, Suresh and Schwarz, Nicholas and Chard, Kyle and Foster, Ian T.},
  title = {Linking scientific instruments and computation: Patterns, technologies, and experiences},
  journal = {Patterns},
  year = {2022},
  volume = {3},
  number = {10},
  pages = {100606},
  doi = {https://doi.org/10.1016/j.patter.2022.100606},
  issn = {2666-3899},
  keywords = {Experiment automation, workflow, Globus, synchrotron light source, big data, machine learning, data fabric, computing fabric, trust fabric, scientific facility},
  url = {https://www.sciencedirect.com/science/article/pii/S2666389922002318},
}

Nazariy Tishchenko, Nicola Ferrier, Joseph A. Insley, Victor A. Mateevitsi, Michael E. Papka, Silvio Rizzi, and Jifu Tan, Toward Bi-directional In Situ Visualization and Analysis of Blood Flow Simulations With Dynamic Deforming Walls [POSTER], In 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV),Oct, 2022
Abstract BibTeX

We present some preliminary work toward bi-directional in situ visualization and analysis of blood flow considering blood cell suspensions and dynamic moving walls to mimic the clot growth process. The work is implemented through integrating multiple open source packages including fluid flow solver, blood cells, and in situ visualization and analysis. It shows that in situ visualization enables us to examine and process the simulation results without writing data to files. In addition, preliminary results on bidirectional steering (still in progress) can accelerate engineers to explore the complicated stress environment experienced by clots of different sizes.
```
@inproceedings{TiFeIn22,
  author = {Tishchenko, Nazariy and Ferrier, Nicola and Insley, Joseph A. and Mateevitsi, Victor A. and Papka, Michael E. and Rizzi, Silvio and Tan, Jifu},
  title = {Toward Bi-directional In Situ Visualization and Analysis of Blood Flow Simulations With Dynamic Deforming Walls [POSTER]},
  booktitle = {2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2022},
  month = oct,
  pages = {1--2},
  doi = {10.1109/LDAV57265.2022.9966389},
  issn = {2832-6512},
}
```
Jason A. Ortiz, Joseph A. Insley, Janet Knowles, Victor A. Mateevitsi, Michael E. Papka, and Silvio Rizzi, Massive Data Visualization Techniques for use in Virtual Reality Devices [POSTER], In 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV),Oct, 2022
Abstract BibTeX

Scientific simulations executed on supercomputers produce massive amounts of data. Visualizing this data is essential to discovery and dissemination, but methods for transforming and displaying such large data visualizations for use in Extended Reality (XR) devices are not commonly supported. We investigated the viability of existing XR applications (i.e., ParaView VR, SummitVR, and Omniverse XR) to display large data visualizations. Our investigations led us to create a proof-of-concept Virtual Reality (VR) application with Unity using Universal Scene Description (USD) files exported from Houdini to display and interact with large time-varying scientific data visualizations. We present our investigations as a basis for future work to display and interact with scientific data visualizations in XR.
```
@inproceedings{OrInKn22,
  author = {Ortiz, Jason A. and Insley, Joseph A. and Knowles, Janet and Mateevitsi, Victor A. and Papka, Michael E. and Rizzi, Silvio},
  title = {Massive Data Visualization Techniques for use in Virtual Reality Devices [POSTER]},
  booktitle = {2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2022},
  month = oct,
  pages = {1--2},
  doi = {10.1109/LDAV57265.2022.9966400},
  issn = {2832-6512},
}
```
Isaac Nealey, Nicola Ferrier, Joseph Insley, Victor A. Mateevitsi, Michael E. Papka, and Silvio Rizzi, Cinema Transfer: A Containerized Visualization Workflow, In High Performance Computing. ISC High Performance 2022 International Workshops, 2022
Abstract BibTeX

We present a containerized workflow demonstrating in situ analysis of simulation data rendered by a ParaView/Catalyst adapter for the generic SENSEI in situ interface, then streamed to a remote site for visualization. We use Cinema, a database approach for navigating the metadata produced in situ. We developed a web socket tool, cinema\_transfer, for transferring the generated cinema databases to a remote machine while the simulation is running. We evaluate the performance of this containerized workflow and identify bottlenecks for large scale runs, in addition to testing identical containers at different sites with differing hardware and Message Passing Interface (MPI) implementations.
```
@inproceedings{NeFeIn22,
  author = {Nealey, Isaac and Ferrier, Nicola and Insley, Joseph and Mateevitsi, Victor A. and Papka, Michael E. and Rizzi, Silvio},
  editor = {Anzt, Hartwig and Bienz, Amanda and Luszczek, Piotr and Baboulin, Marc},
  title = {Cinema Transfer: A Containerized Visualization Workflow},
  booktitle = {High Performance Computing. ISC High Performance 2022 International Workshops},
  year = {2022},
  pages = {324--343},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-031-23220-6},
}
```
Thomas Marrinan, Jifu Tan, Joseph A. Insley, Alina Kanayinkal, and Michael E. Papka, Interactive Virtual Reality Exploration of Large-Scale Datasets Using Omnidirectional Stereo Images, In Advances in Visual Computing, 2022
Abstract BibTeX

Virtual reality offers unique affordances that can benefit the scientific discovery process. However, virtual reality applications must maintain very high frame rates to provide immersion and prevent adverse events such as visual fatigue and motion sickness. Maintaining high frame rates can be challenging when visualizing scientific data that is large in scale. One successful technique for enabling interactive exploration of large-scale datasets is to create a large image collection from a structured sampling of camera positions, time steps, and visualization operators. This paper highlights our work to adapt this technique for virtual reality, and uses two authentic scientific datasets – a) a large-scale simulation of cancer cell transport and capture in a microfluidic device and b) a large-scale molecular dynamics simulation of graphene for creating extremely low friction interactions. We create a collection of omnidirectional stereoscopic images (three-dimensional surround-view panoramas), each of which captures all possible view angles from a given location. Therefore, virtual reality devices can always render local movements at full frame rates without loading a new image from the collection.
```
@inproceedings{MaTaIn22,
  author = {Marrinan, Thomas and Tan, Jifu and Insley, Joseph A. and Kanayinkal, Alina and Papka, Michael E.},
  editor = {Bebis, George and Li, Bo and Yao, Angela and Liu, Yang and Duan, Ye and Lau, Manfred and Khadka, Rajiv and Crisan, Ana and Chang, Remco},
  title = {Interactive Virtual Reality Exploration of Large-Scale Datasets Using Omnidirectional Stereo Images},
  booktitle = {Advances in Visual Computing},
  year = {2022},
  pages = {115--128},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-031-20713-6},
}
```
Murali Emani, Zhen Xie, Siddhisanket Raskar, Varuni Sastry, William Arnold, Bruce Wilson, Rajeev Thakur, Venkatram Vishwanath, Zhengchun Liu, Michael E. Papka, Cindy Orozco Bohorquez, Rick Weisner, Karen Li, Yongning Sheng, Yun Du, Jian Zhang, Alexander Tsyplikhin, Gurdaman Khaira, Jeremy Fowers, Ramakrishnan Sivakumar, Victoria Godsoe, Adrian Macias, Chetan Tekur, and Matthew Boyd, A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads, In 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 2022
Abstract BibTeX

Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to advance science. High-performance computing centers are evaluating emerging novel hardware accelerators to efficiently run AI-driven science applications. With a wide diversity in the hardware architectures and software stacks of these systems, it is challenging to understand how these accelerators perform. The state-of-the-art in the evaluation of deep learning workloads primarily focuses on CPUs and GPUs. In this paper, we present an overview of dataflow-based novel AI accelerators from SambaNova, Cerebras, Graphcore, and Groq. We present a first-of-a-kind evaluation of these accelerators with diverse workloads, such as Deep Learning (DL) primitives, benchmark models, and scientific machine learning applications. We also evaluate the performance of collective communication, which is key for distributed DL implementation, along with a study of scaling efficiency. We then discuss key insights, challenges, and opportunities in integrating these novel AI accelerators in supercomputing systems.
```
@inproceedings{EmXiRa22,
  author = {Emani, Murali and Xie, Zhen and Raskar, Siddhisanket and Sastry, Varuni and Arnold, William and Wilson, Bruce and Thakur, Rajeev and Vishwanath, Venkatram and Liu, Zhengchun and Papka, Michael E. and Bohorquez, Cindy Orozco and Weisner, Rick and Li, Karen and Sheng, Yongning and Du, Yun and Zhang, Jian and Tsyplikhin, Alexander and Khaira, Gurdaman and Fowers, Jeremy and Sivakumar, Ramakrishnan and Godsoe, Victoria and Macias, Adrian and Tekur, Chetan and Boyd, Matthew},
  title = {A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads},
  booktitle = {2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)},
  year = {2022},
  pages = {13--25},
  doi = {10.1109/pmbs56514.2022.00007},
}
```
Pratool Bharti, Priyanjani Chandra, Michael E. Papka, and David Koop, An Edge Map based Ensemble Solution to Detect Water Level in Stream, arXiv preprint arXiv:2201.06098, 2022
Abstract arXiv BibTeX

Flooding is one of the most dangerous weather events today. Between 2015−2019, on average, flooding has caused more than 130 deaths every year in the USA alone. The devastating nature of flood necessitates the continuous monitoring of water level in the rivers and streams to detect the incoming flood. In this work, we have designed and implemented an efficient vision-based ensemble solution to continuously detect the water level in the creek. Our solution adapts template matching algorithm to find the region of interest by leveraging edge maps, and combines two parallel approach to identify the water level. While first approach fits a linear regression model in edge map to identify the water line, second approach uses a split sliding window to compute the sum of squared difference in pixel intensities to find the water surface. We evaluated the proposed system on 4306 images collected between 3rd October and 18th December in 2019 with the frequency of 1 image in every 10 minutes. The system exhibited low error rate as it achieved 4.8, 3.1% and 0.92 scores for MAE, MAPE and R2 evaluation metrics, respectively. We believe the proposed solution is very practical as it is pervasive, accurate, doesn’t require installation of any additional infrastructure in the water body and can be easily adapted to other locations.
```
@article{BhChPa22,
  author = {Bharti, Pratool and Chandra, Priyanjani and Papka, Michael E. and Koop, David},
  title = {An Edge Map based Ensemble Solution to Detect Water Level in Stream},
  journal = {arXiv preprint arXiv:2201.06098},
  year = {2022},
  archiveprefix = {arXiv},
  date-modified = {2022-02-05 06:58:25 -0600},
  eprint = {2201.06098},
  primaryclass = {cs.CV},
}
```

2021

Anthony Bucaro, Connor Murphy, Nicola Ferrier, Joseph Insley, Victor Mateevitsi, Michael E. Papka, Silvio Rizzi, and Jifu Tan, Instrumenting Multiphysics Blood Flow Simulation Codes for In Situ Visualization and Analysis, In 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), 2021

BibTeX

@inproceedings{BuMuFe21,
  author = {Bucaro, Anthony and Murphy, Connor and Ferrier, Nicola and Insley, Joseph and Mateevitsi, Victor and Papka, Michael E. and Rizzi, Silvio and Tan, Jifu},
  title = {Instrumenting Multiphysics Blood Flow Simulation Codes for In Situ Visualization and Analysis},
  booktitle = {2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2021},
  pages = {88--89},
  organization = {IEEE},
}

Murali Emani, Venkatram Vishwanath, Corey Adams, Michael E. Papka, Rick Stevens, Laura Florescu, Sumti Jairath, William Liu, Tejas Nama, and Arvind Sujeeth, Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture, Computing in Science & Engineering, 2021

BibTeX

@article{EmViAd21,
  author = {Emani, Murali and Vishwanath, Venkatram and Adams, Corey and Papka, Michael E. and Stevens, Rick and Florescu, Laura and Jairath, Sumti and Liu, William and Nama, Tejas and Sujeeth, Arvind},
  title = {{Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture}},
  journal = {Computing in Science & Engineering},
  year = {2021},
  volume = {23},
  number = {2},
  pages = {114--119},
  doi = {10.1109/mcse.2021.3057203},
}

Michael A. Fazio, Marcus Babzien, Sandra G. Biedron, Kevin A. Brown, Julian Chen, Mikhail G. Fedurin, Alan J. Hurd, Junjie J. Li, David E. Martin, Manuel Martínez-Ramón, David J. Monk, N.A. Moody, Mark A. Palmer, Michael E. Papka, Rohit Prasankumar, Salvador I. Sosa, Christine Sweeney, Tyler Talbott, and Jing Tao, Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System, In Proc. IPAC’21,Aug, 2021

Abstract BibTeX

A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.

@inproceedings{FaBaBi21,
  author = {Fazio, Michael A. and Babzien, Marcus and Biedron, Sandra G. and Brown, Kevin A. and Chen, Julian and Fedurin, Mikhail G. and Hurd, Alan J. and Li, Junjie J. and Martin, David E. and Martínez-Ramón, Manuel and Monk, David J. and Moody, N.A. and Palmer, Mark A. and Papka, Michael E. and Prasankumar, Rohit and Sosa, Salvador I. and Sweeney, Christine and Talbott, Tyler and Tao, Jing},
  title = {{Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System}},
  booktitle = {Proc. IPAC'21},
  year = {2021},
  month = aug,
  number = {12},
  pages = {906--908},
  publisher = {JACoW Publishing, Geneva, Switzerland},
  series = {International Particle Accelerator Conference},
  doi = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286},
  isbn = {978-3-95450-214-1},
  issn = {2673-5490},
  keywords = {electron, network, experiment, real-time, laser},
  venue = {Campinas, SP, Brazil},
}

Yuping Fan, Zhiling Lan, J. Taylor Childers, Paul Rich, William Allcock, and Michael E. Papka, Deep reinforcement agent for scheduling in HPC, In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2021
BibTeX
```
@inproceedings{FaLaCh21,
  author = {Fan, Yuping and Lan, Zhiling and Childers, J. Taylor and Rich, Paul and Allcock, William and Papka, Michael E.},
  title = {Deep reinforcement agent for scheduling in HPC},
  booktitle = {2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
  year = {2021},
  pages = {807--816},
  organization = {IEEE},
}
```
Yuping Fan, Paul Rich, William Allcock, Michael Papka, and Zhiling Lan, Hybrid Workload Scheduling on HPC Systems, arXiv preprint arXiv:2109.05412, 2021
BibTeX
```
@article{FaRiAl21,
  author = {Fan, Yuping and Rich, Paul and Allcock, William and Papka, Michael and Lan, Zhiling},
  title = {Hybrid Workload Scheduling on HPC Systems},
  journal = {arXiv preprint arXiv:2109.05412},
  year = {2021},
}
```

Bharat Kale, Maoyuan Sun, and Michael E. Papka, Direct Ordering: A Direct Manipulation Based Ordering Technique, In 26th International Conference on Intelligent User Interfaces - Companion, Fourth Workshop on Exploratory Search and Interactive Data Analytics (ESIDA), 2021

Abstract BibTeX

The ordering of data can reveal patterns in various data visualizations. It significantly affects the expressiveness of visualizations by making them clear or cluttered. Clutter makes it hard for users to perceive patterns, even for a small dataset. Almost all visualization tools available today support ordering through indirect manipulation techniques where users rely on a widget (e.g., a button or a checkbox) to perform ordering in some predefined way. For example, using widgets for sorting a selected subset of data involves creating widgets to filter the data, highlight the filtered data, and then apply the ordering. This work presents a new interaction technique based on direct manipulation, which supports flexible visual ordering. This direct manipulation-based interaction helps to visually filter the data and then order them. We demonstrate how the proposed interaction technique works with bar charts and matrices, two heavily used visualization techniques that involve ordering.

@incollection{KaSuPa21,
  author = {Kale, Bharat and Sun, Maoyuan and Papka, Michael E.},
  editor = {Glowacka, Dorota and Milios, Evangelos and Soto, Axel J. and Mokryn, Osnat and Paulovich, Fernando V. and Parra, Denis},
  title = {Direct Ordering: A Direct Manipulation Based Ordering Technique},
  booktitle = {26th International Conference on Intelligent User Interfaces - Companion, Fourth Workshop on Exploratory Search and Interactive Data Analytics (ESIDA)},
  year = {2021},
  pages = {18–20},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {IUI '21 Companion},
  doi = {10.1145/3397482.3450711},
  isbn = {9781450380188},
  keywords = {personalisation, data analytics, exploratory search, interactive search},
  location = {College Station, TX, USA},
  numpages = {3},
  url = {https://doi.org/10.1145/3397482.3450711},
}

Zhengchun Liu, Rajkumar Kettimuthu, Michael E. Papka, and Ian Foster, BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes, arXiv preprint arXiv:2106.12091, 2021
BibTeX
```
@article{LiKePa21,
  author = {Liu, Zhengchun and Kettimuthu, Rajkumar and Papka, Michael E. and Foster, Ian},
  title = {BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes},
  journal = {arXiv preprint arXiv:2106.12091},
  year = {2021},
}
```
Riley Lipinksi, Kenneth Moreland, Michael E. Papka, and Thomas Marrinan, Poster: GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications, In 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), 2021
BibTeX
```
@inproceedings{LiMoPa21,
  author = {Lipinksi, Riley and Moreland, Kenneth and Papka, Michael E. and Marrinan, Thomas},
  title = {Poster: GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications},
  booktitle = {2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2021},
  pages = {43--52},
  organization = {IEEE},
}
```

Travis Mackoy, Bharat Kale, Michael E. Papka, and Ralph A. Wheeler, viewSq, a Visual Molecular Dynamics (VMD) module for calculating, analyzing, and visualizing X-ray and neutron structure factors from atomistic simulations, Computer Physics Communications, 2021

BibTeX

@article{MaKaPa21,
  author = {Mackoy, Travis and Kale, Bharat and Papka, Michael E. and Wheeler, Ralph A.},
  title = {viewSq, a Visual Molecular Dynamics (VMD) module for calculating, analyzing, and visualizing X-ray and neutron structure factors from atomistic simulations},
  journal = {Computer Physics Communications},
  year = {2021},
  volume = {264},
  pages = {107881},
  publisher = {North-Holland},
}

Thomas Marrinan, and Michael E. Papka, Real-Time Omnidirectional Stereo Rendering: Generating 360° Surround-View Panoramic Images for Comfortable Immersive Viewing, IEEE Transactions on Visualization and Computer Graphics, 2021
Abstract BibTeX

Surround-view panoramic images and videos have become a popular form of media for interactive viewing on mobile devices and virtual reality headsets. Viewing such media provides a sense of immersion by allowing users to control their view direction and experience an entire environment. When using a virtual reality headset, the level of immersion can be improved by leveraging stereoscopic capabilities. Stereoscopic images are generated in pairs, one for the left eye and one for the right eye, and result in providing an important depth cue for the human visual system. For computer generated imagery, rendering proper stereo pairs is well known for a fixed view. However, it is much more difficult to create omnidirectional stereo pairs for a surround-view projection that work well when looking in any direction. One major drawback of traditional omnidirectional stereo images is that they suffer from binocular misalignment in the peripheral vision as a user’s view direction approaches the zenith / nadir (north / south pole) of the projection sphere. This paper presents a real-time geometry-based approach for omnidirectional stereo rendering that fits into the standard rendering pipeline. Our approach includes tunable parameters that enable pole merging - a reduction in the stereo effect near the poles that can minimize binocular misalignment. Results from a user study indicate that pole merging reduces visual fatigue and discomfort associated with binocular misalignment without inhibiting depth perception.
```
@article{MaPa21,
  author = {Marrinan, Thomas and Papka, Michael E.},
  title = {Real-Time Omnidirectional Stereo Rendering: Generating 360° Surround-View Panoramic Images for Comfortable Immersive Viewing},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2021},
  volume = {27},
  number = {5},
  pages = {2587--2596},
  doi = {10.1109/tvcg.2021.3067780},
}
```
David J. Monk, Marcus Babzien, Sandra G. Biedron, Kevin A. Brown, Michael A. Fazio, David E. Martin, Manuel Martínez-Ramon, Mark A. Palmer, Michael E. Papka, Salvador I. Sosa, Tyler Talbott, and Jing Tao, Surrogate Modeling for MUED with Neural Networks, In Proc. IPAC’21,Aug, 2021
Abstract BibTeX

Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.
```
@inproceedings{MoBaBi21,
  author = {Monk, David J. and Babzien, Marcus and Biedron, Sandra G. and Brown, Kevin A. and Fazio, Michael A. and Martin, David E. and Martínez-Ramon, Manuel and Palmer, Mark A. and Papka, Michael E. and Sosa, Salvador I. and Talbott, Tyler and Tao, Jing},
  title = {{Surrogate Modeling for MUED with Neural Networks}},
  booktitle = {Proc. IPAC'21},
  year = {2021},
  month = aug,
  number = {12},
  pages = {970--971},
  publisher = {JACoW Publishing, Geneva, Switzerland},
  series = {International Particle Accelerator Conference},
  doi = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314},
  isbn = {978-3-95450-214-1},
  issn = {2673-5490},
  keywords = {electron, experiment, gun, network, operation},
  venue = {Campinas, SP, Brazil},
}
```
Khairi Reda, Amey A. Salvi, Jack Gray, and Michael E. Papka, Color Nameability Predicts Inference Accuracy in Spatial Visualizations, Computer Graphics Forum, 2021
Abstract BibTeX

Color encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive and linguistic associations whose role in data interpretation remains underexplored. We study how two linguistic factors, name salience and name variation, affect people’s ability to draw inferences from spatial visualizations. In two experiments, we found that participants are better at interpreting visualizations when viewing colors with more salient names (e.g., prototypical ‘blue’, ‘yellow’, and ‘red’ over ‘teal’, ‘beige’, and ‘maroon’). The effect was robust across four visualization types, but was more pronounced in continuous (e.g., smooth geographical maps) than in similar discrete representations (e.g., choropleths). Participants’ accuracy also improved as the number of nameable colors increased, although the latter had a less robust effect. Our findings suggest that color nameability is an important design consideration for quantitative colormaps, and may even outweigh traditional perceptual metrics. In particular, we found that the linguistic associations of color are a better predictor of performance than the perceptual properties of those colors. We discuss the implications and outline research opportunities. The data and materials for this study are available at https://osf.io/asb7n
```
@article{ReSaGr21,
  author = {Reda, Khairi and Salvi, Amey A. and Gray, Jack and Papka, Michael E.},
  title = {Color Nameability Predicts Inference Accuracy in Spatial Visualizations},
  journal = {Computer Graphics Forum},
  year = {2021},
  volume = {40},
  number = {3},
  pages = {49--60},
  doi = {https://doi.org/10.1111/cgf.14288},
  keywords = {CCS Concepts, • Human-centered computing → Empirical studies in visualization},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14288},
}
```
Pambayun Savira, Thomas Marrinan, and Michael E. Papka, Poster: Writing, Running, and Analyzing Large-scale Scientific Simulations with Jupyter Notebooks, In 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), 2021
BibTeX Poster
```
@inproceedings{SaMaPa21,
  author = {Savira, Pambayun and Marrinan, Thomas and Papka, Michael E.},
  title = {Poster: Writing, Running, and Analyzing Large-scale Scientific Simulations with Jupyter Notebooks},
  booktitle = {2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2021},
  pages = {90--91},
  organization = {IEEE},
}
```
Michael Salim, Thomas Uram, J. Taylor Childers, Venkat Vishwanath, and Michael E. Papka, Toward real-time analysis of experimental science workloads on geographically distributed supercomputers, arXiv preprint arXiv:2105.06571, 2021
Abstract arXiv BibTeX

Massive upgrades to science infrastructure are driving data velocities upwards while stimulating adoption of increasingly data-intensive analytics. While next-generation exascale supercomputers promise strong support for I/O-intensive workflows, HPC remains largely untapped by live experiments, because data transfers and disparate batch-queueing policies are prohibitive when faced with scarce instrument time. To bridge this divide, we introduce Balsam: a distributed orchestration platform enabling workflows at the edge to securely and efficiently trigger analytics tasks across a user-managed federation of HPC execution sites. We describe the architecture of the Balsam service, which provides a workflow management API, and distributed sites that provision resources and schedule scalable, fault-tolerant execution. We demonstrate Balsam in efficiently scaling real-time analytics from two DOE light sources simultaneously onto three supercomputers (Theta, Summit, and Cori), while maintaining low overheads for on-demand computing, and providing a Python library for seamless integration with existing ecosystems of data analysis tools.
```
@article{SaUrCh21,
  author = {Salim, Michael and Uram, Thomas and Childers, J. Taylor and Vishwanath, Venkat and Papka, Michael E.},
  title = {Toward real-time analysis of experimental science workloads on geographically distributed supercomputers},
  journal = {arXiv preprint arXiv:2105.06571},
  year = {2021},
  archiveprefix = {arXiv},
  eprint = {2105.06571},
  primaryclass = {cs.DC},
}
```

2020

Sreeranjani Ramprakash, Haritha Siddabathuni Som, Mark Fahey, Emily Shemon, David Martin, Katherine Riley, Kalyan Kumaran, Laura Wolf, Doug Waldron, and Eric Pershey, 2020 Operational Assessment Report-Argonne Leadership Computing Facility, 2020

BibTeX

@techreport{RaSoFa20,
  author = {Ramprakash, Sreeranjani and Som, Haritha Siddabathuni and Fahey, Mark and Shemon, Emily and Martin, David and Riley, Katherine and Kumaran, Kalyan and Wolf, Laura and Waldron, Doug and Pershey, Eric},
  title = {2020 Operational Assessment Report-Argonne Leadership Computing Facility},
  year = {2020},
  institution = {Argonne National Laboratory (ANL), Argonne, IL (United States)},
}

Zhengchun Liu, Ryan Lewis, Rajkumar Kettimuthu, Kevin Harms, Philip Carns, Nageswara Rao, Ian Foster, and Michael E. Papka, Characterization and identification of HPC applications at leadership computing facility, In Proceedings of the 34th ACM International Conference on Supercomputing, 2020
BibTeX
```
@inproceedings{LiLeKe20,
  author = {Liu, Zhengchun and Lewis, Ryan and Kettimuthu, Rajkumar and Harms, Kevin and Carns, Philip and Rao, Nageswara and Foster, Ian and Papka, Michael E.},
  title = {Characterization and identification of HPC applications at leadership computing facility},
  booktitle = {Proceedings of the 34th ACM International Conference on Supercomputing},
  year = {2020},
  pages = {1--12},
}
```
Ryan D. Lewis, Zhengchun Liu, Rajkumar Kettimuthu, and Michael E. Papka, Log-based identification, classification, and behavor prediction of hpc applications, In In HPCSYSPROS20: HPC System Professionals Workshop, Atlanta, GA, 2020
BibTeX
```
@inproceedings{LeLiKe20,
  author = {Lewis, Ryan D. and Liu, Zhengchun and Kettimuthu, Rajkumar and Papka, Michael E.},
  title = {Log-based identification, classification, and behavor prediction of hpc applications},
  booktitle = {In HPCSYSPROS20: HPC System Professionals Workshop, Atlanta, GA},
  year = {2020},
}
```
James J. Hack, and Michael E. Papka, The US high-performance computing consortium in the fight against COVID-19, Computing in Science & Engineering, 2020
BibTeX
```
@article{HaPa20,
  author = {Hack, James J. and Papka, Michael E.},
  title = {The US high-performance computing consortium in the fight against COVID-19},
  journal = {Computing in Science & Engineering},
  year = {2020},
  volume = {22},
  number = {6},
  pages = {75--80},
  publisher = {IEEE},
}
```

Scott Collis, Pete Beckman, Eugene Kelly, Charles Catlett, Rajesh Sankaran, Ikay Altintas, Jim Olds, Nicola Ferrier, Seongha Park, Yongho Kim, and Michael E. Papka, Introducing Sage: Cyberinfrastructure for Sensing at the Edge., In EGU General Assembly, 2020

BibTeX

@inproceedings{CoBeKe20,
  author = {Collis, Scott and Beckman, Pete and Kelly, Eugene and Catlett, Charles and Sankaran, Rajesh and Altintas, Ikay and Olds, Jim and Ferrier, Nicola and Park, Seongha and Kim, Yongho and Papka, Michael E.},
  title = {Introducing Sage: Cyberinfrastructure for Sensing at the Edge.},
  booktitle = {EGU General Assembly},
  year = {2020},
  pages = {12320},
  doi = {doi.org/10.5194/egusphere-egu2020-12320},
}

Priyanjani Chandra, Pratool Bharti, and Michael Papka, Poster: A Computer Vision and AI Based Solution to Determine the Change in Water Level in Stream, 2020
BibTeX
```
@misc{ChBhPa20,
  author = {Chandra, Priyanjani and Bharti, Pratool and Papka, Michael},
  title = {Poster: A Computer Vision and AI Based Solution to Determine the Change in Water Level in Stream},
  year = {2020},
}
```
Charlie Catlett, Pete Beckman, Nicola Ferrier, Howard Nusbaum, Michael E. Papka, Marc G. Berman, and Rajesh Sankaran, Measuring Cities with Software-Defined Sensors, Journal of Social Computing, 2020
BibTeX
```
@article{CaBeFe20,
  author = {Catlett, Charlie and Beckman, Pete and Ferrier, Nicola and Nusbaum, Howard and Papka, Michael E. and Berman, Marc G. and Sankaran, Rajesh},
  title = {Measuring Cities with Software-Defined Sensors},
  journal = {Journal of Social Computing},
  year = {2020},
  volume = {1},
  number = {1},
  pages = {14--27},
  publisher = {Tup},
}
```

2019

Rick Stevens, Jini Ramprakash, Paul Messina, Michael Papka, and Katherine Riley, Aurora: Argonne’s next-generation exascale supercomputer, 2019
BibTeX
```
@techreport{StRaMe19,
  author = {Stevens, Rick and Ramprakash, Jini and Messina, Paul and Papka, Michael and Riley, Katherine},
  title = {Aurora: Argonne's next-generation exascale supercomputer},
  year = {2019},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```

FNU Shilpika, Bethany Lusch, Murali Emani, Venkatram Vishwanath, Michael E. Papka, and Kwan-Liu Ma, Mela: A visual analytics tool for studying multifidelity hpc system logs, In 2019 IEEE/ACM Industry/University Joint International Workshop on Data-center Automation, Analytics, and Control (DAAC), 2019

BibTeX

@inproceedings{ShLuEm19,
  author = {Shilpika, FNU and Lusch, Bethany and Emani, Murali and Vishwanath, Venkatram and Papka, Michael E. and Ma, Kwan-Liu},
  title = {Mela: A visual analytics tool for studying multifidelity hpc system logs},
  booktitle = {2019 IEEE/ACM Industry/University Joint International Workshop on Data-center Automation, Analytics, and Control (DAAC)},
  year = {2019},
  pages = {13--18},
  organization = {IEEE},
}

Sergei Shudler, Nicola Ferrier, Joseph Insley, Michael E. Papka, Saumil Patel, and Silvio Rizzi, Fast Mesh Validation in Combustion Simulations through In-Situ Visualization, In Eurographics Symposium on Parallel Graphics and Visualization, 2019

BibTeX

@inproceedings{ShFeIn19b,
  author = {Shudler, Sergei and Ferrier, Nicola and Insley, Joseph and Papka, Michael E. and Patel, Saumil and Rizzi, Silvio},
  editor = {Childs, Hank and Frey, Steffen},
  title = {{Fast Mesh Validation in Combustion Simulations through In-Situ Visualization}},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
  year = {2019},
  publisher = {The Eurographics Association},
  doi = {10.2312/pgv.20191105},
  isbn = {978-3-03868-079-6},
  issn = {1727-348x},
}

Sergei Shudler, Nicola Ferrier, Joseph Insley, Michael E. Papka, and Silvio Rizzi, Spack meets singularity: creating movable in-situ analysis stacks with ease, In Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2019
BibTeX
```
@inproceedings{ShFeIn19a,
  author = {Shudler, Sergei and Ferrier, Nicola and Insley, Joseph and Papka, Michael E. and Rizzi, Silvio},
  title = {Spack meets singularity: creating movable in-situ analysis stacks with ease},
  booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
  year = {2019},
  pages = {34--38},
}
```
Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, and Michael E. Papka, Balsam: Automated scheduling and execution of dynamic, data-intensive hpc workflows, arXiv preprint arXiv:1909.08704, 2019
BibTeX
```
@article{SaUrCh19b,
  author = {Salim, Michael A. and Uram, Thomas D. and Childers, J. Taylor and Balaprakash, Prasanna and Vishwanath, Venkatram and Papka, Michael E.},
  title = {Balsam: Automated scheduling and execution of dynamic, data-intensive hpc workflows},
  journal = {arXiv preprint arXiv:1909.08704},
  year = {2019},
}
```
Michael Salim, Thomas Uram, J. Taylor Childers, Venkatram Vishwanath, and Michael Papka, Balsam: Near real-time experimental data analysis on supercomputers, In 2019 IEEE/ACM 1st Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP), 2019
BibTeX
```
@inproceedings{SaUrCh19a,
  author = {Salim, Michael and Uram, Thomas and Childers, J. Taylor and Vishwanath, Venkatram and Papka, Michael},
  title = {Balsam: Near real-time experimental data analysis on supercomputers},
  booktitle = {2019 IEEE/ACM 1st Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP)},
  year = {2019},
  pages = {26--31},
  organization = {IEEE},
}
```
Allen Sanderson, John Schmidt, Alan Humphrey, Michael Papka, and Robert Sisneros, In Situ Visualization of Performance Metrics in Multiple Domains, In 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), 2019
BibTeX
```
@inproceedings{SaScHu19,
  author = {Sanderson, Allen and Schmidt, John and Humphrey, Alan and Papka, Michael and Sisneros, Robert},
  title = {In Situ Visualization of Performance Metrics in Multiple Domains},
  booktitle = {2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools)},
  year = {2019},
  pages = {62--69},
  organization = {IEEE},
}
```
Katherine Riley, Michael E. Papka, Jim Collins, Nils Heinonen, and Beth Cerny, 2019 ALCF Science Report, 2019
BibTeX
```
@techreport{RiPaCo19,
  author = {Riley, Katherine and Papka, Michael E. and Collins, Jim and Heinonen, Nils and Cerny, Beth},
  title = {2019 ALCF Science Report},
  year = {2019},
  institution = {Argonne National Laboratory (ANL), Argonne, IL (United States)},
}
```
Khairi Reda, and Michael E. Papka, Evaluating Gradient Perception in Color-Coded Scalar Fields, In 2019 IEEE Visualization Conference (VIS), 2019
Abstract BibTeX

Color mapping is a commonly used technique for visualizing scalar fields. While there exists advice for choosing effective colormaps, it is unclear if current guidelines apply equally across task types. We study the perception of gradients and evaluate the effectiveness of three colormaps at depicting gradient magnitudes. In a crowd-sourced experiment, we determine the just-noticeable differences (JNDs) at which participants can reliably compare and judge variations in gradient between two scalar fields. We find that participants exhibited lower JNDs with a diverging (cool-warm) or a spectral (rainbow) scheme, as compared with a monotonic-luminance colormap (viridis). The results support a hypothesis that apparent discontinuities in the color ramp may help viewers discern subtle structural differences in gradient. We discuss these findings and highlight future research directions for colormap evaluation.
```
@inproceedings{RePa19,
  author = {Reda, Khairi and Papka, Michael E.},
  title = {Evaluating Gradient Perception in Color-Coded Scalar Fields},
  booktitle = {2019 IEEE Visualization Conference (VIS)},
  year = {2019},
  pages = {271--275},
  doi = {10.1109/visual.2019.8933760},
}
```
Thomas Marrinan, Silvio Rizzi, Joseph A. Insley, Lance Long, Luc Renambot, and Michael E. Papka, Pxstream: Remote visualization for distributed rendering frameworks, In 2019 IEEE 9th Symposium on Large Data Analysis and Visualization (LDAV), 2019
BibTeX
```
@inproceedings{MaRiIn19,
  author = {Marrinan, Thomas and Rizzi, Silvio and Insley, Joseph A. and Long, Lance and Renambot, Luc and Papka, Michael E.},
  title = {Pxstream: Remote visualization for distributed rendering frameworks},
  booktitle = {2019 IEEE 9th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2019},
  pages = {37--41},
  organization = {IEEE},
}
```

Travis Mackoy, Bharat Kale, Michael Papka, and Ralph Wheeler, viewSq: VMD module for visualizing and quantifying periodic atomic ordering underlying static structure factors from molecular dynamics simulations, In Abstracts of papers of the American Chemical Society, 2019

BibTeX

@inproceedings{MaKaPa19,
  author = {Mackoy, Travis and Kale, Bharat and Papka, Michael and Wheeler, Ralph},
  title = {viewSq: VMD module for visualizing and quantifying periodic atomic ordering underlying static structure factors from molecular dynamics simulations},
  booktitle = {Abstracts of papers of the American Chemical Society},
  year = {2019},
  volume = {258},
  organization = {Amer Chemical Soc 1155 16th St, Nw, Washington, DC 20036 Usa},
}

Thomas Marrinan, Greg Eisenhauer, Matthew Wolf, Joseph A. Insley, Silvio Rizzi, and Michael E. Papka, Parallel streaming between heterogeneous HPC resources for real-time analysis, Journal of Computational Science, 2019
BibTeX
```
@article{MaEiWo19,
  author = {Marrinan, Thomas and Eisenhauer, Greg and Wolf, Matthew and Insley, Joseph A. and Rizzi, Silvio and Papka, Michael E.},
  title = {Parallel streaming between heterogeneous HPC resources for real-time analysis},
  journal = {Journal of Computational Science},
  year = {2019},
  volume = {31},
  pages = {163--171},
  publisher = {Elsevier},
}
```
Yuping Fan, Zhiling Lan, Paul Rich, William E. Allcock, Michael E. Papka, Brian Austin, and David Paul, Scheduling beyond CPUs for HPC, In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, 2019
BibTeX
```
@inproceedings{FaLaRi19,
  author = {Fan, Yuping and Lan, Zhiling and Rich, Paul and Allcock, William E. and Papka, Michael E. and Austin, Brian and Paul, David},
  title = {Scheduling beyond CPUs for HPC},
  booktitle = {Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing},
  year = {2019},
  pages = {97--108},
}
```

Mark R. Fahey, Yuri Alexeev, William Allcock, Benjamin S. Allen, Ramesh Balakrishnan, Anouar Benali, Liza Booker, Ashley Boyle, Laural Briggs, Edouard Brooks, Phil Carns, Beth Cerny, Andrew Cherry, Lisa Childers, Sudheer Chunduri, Richard Coffey, James Collins, Paul Coffman, Susan Coghlan, Kathy DiBennardi, Ginny Doyle, Hal Finkel, Graham Fletcher, Marta Garcia, Ira Goldberg, Cheetah Goletz, Susan Gregurich, Kevin Harms, Carissa Holohan, Joseph A. Insley, Tommie Jackson, Janet Jaseckas, Elise Jennings, Derek Jensen, Wei Jiang, Margaret Kaczmarski, Chris Knight, Janet Knowles, Kalyan Kumaran, Ti Leggett, Ben Lenard, Anping Liu, Ray Loy, Preeti Malakar, Avanthi Mantrala, David E. Martin, Guillermo Mayorga, Gordon McPheeters, Paul Messina, Ryan Milner, Vitali Morozov, Zachary Nault, Denise Nelson, Jack O’Connell, James Osborn, Michael E. Papka, Scott Parker, Pragnesh Patel, Saumil Patel, Eric Pershey, Renee Plzak, Adrian Pope, Jared Punzel, Sreeranjani Ramprakash, John ‘Skip’ Reddy, Paul Rich, Katherine Riley, Silvio Rizzi, George Rojas, Nichols A. Romero, Robert Scott, Adam Scovel, William Scullin, Emily Shemon, Haritha Siddabathuni Som, Joan Stover, Mirek Suliba, Brian Toonen, Tom Uram, Alvaro Vazquez-Mayagoitia, Venkatram Vishwanath, R. Douglas Waldron, Gabe West, Timothy J. Williams, Darin Wills, Laura Wolf, Wanda Woods, and Michael Zhang, Theta and Mira at Argonne National Laboratory, In Contemporary High Performance Computing, 2019

BibTeX

@incollection{FaAlAl19,
  author = {Fahey, Mark R. and Alexeev, Yuri and Allcock, William and Allen, Benjamin S. and Balakrishnan, Ramesh and Benali, Anouar and Booker, Liza and Boyle, Ashley and Briggs, Laural and Brooks, Edouard and Carns, Phil and Cerny, Beth and Cherry, Andrew and Childers, Lisa and Chunduri, Sudheer and Coffey, Richard and Collins, James and Coffman, Paul and Coghlan, Susan and DiBennardi, Kathy and Doyle, Ginny and Finkel, Hal and Fletcher, Graham and Garcia, Marta and Goldberg, Ira and Goletz, Cheetah and Gregurich, Susan and Harms, Kevin and Holohan, Carissa and Insley, Joseph A. and Jackson, Tommie and Jaseckas, Janet and Jennings, Elise and Jensen, Derek and Jiang, Wei and Kaczmarski, Margaret and Knight, Chris and Knowles, Janet and Kumaran, Kalyan and Leggett, Ti and Lenard, Ben and Liu, Anping and Loy, Ray and Malakar, Preeti and Mantrala, Avanthi and Martin, David E. and Mayorga, Guillermo and McPheeters, Gordon and Messina, Paul and Milner, Ryan and Morozov, Vitali and Nault, Zachary and Nelson, Denise and O’Connell, Jack and Osborn, James and Papka, Michael E. and Parker, Scott and Patel, Pragnesh and Patel, Saumil and Pershey, Eric and Plzak, Renee and Pope, Adrian and Punzel, Jared and Ramprakash, Sreeranjani and Reddy, John ‘Skip’ and Rich, Paul and Riley, Katherine and Rizzi, Silvio and Rojas, George and Romero, Nichols A. and Scott, Robert and Scovel, Adam and Scullin, William and Shemon, Emily and Som, Haritha Siddabathuni and Stover, Joan and Suliba, Mirek and Toonen, Brian and Uram, Tom and Vazquez-Mayagoitia, Alvaro and Vishwanath, Venkatram and Waldron, R. Douglas and West, Gabe and Williams, Timothy J. and Wills, Darin and Wolf, Laura and Woods, Wanda and Zhang, Michael},
  title = {{Theta and Mira at Argonne National Laboratory}},
  booktitle = {Contemporary High Performance Computing},
  year = {2019},
  pages = {31--61},
  publisher = {CRC Press},
}

William E. Allcock, Benjamin S. Allen, Rachana Ananthakrishnan, Ben Blaiszik, Kyle Chard, Ryan Chard, Ian Foster, Lukasz Lacinski, Michael E. Papka, and Rick Wagner, Petrel: A Programmatically Accessible Research Data Service, In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), 2019

Abstract BibTeX

We report on our experiences deploying and operating Petrel, a data service designed to support science projects that must organize and distribute large quantities of data. Building on a high-performance 3.2 PB parallel file system and embedded in Argonne National Laboratory’s 100+ Gbps network fabric, Petrel leverages Science DMZ concepts and Globus APIs to provide application scientists with a high-speed, highly connected, and programmatically controllable data store. We describe Petrel’s design, implementation, and usage and give representative examples to illustrate the many different ways in which scientists have employed the system.

@incollection{AlAlAn19,
  author = {Allcock, William E. and Allen, Benjamin S. and Ananthakrishnan, Rachana and Blaiszik, Ben and Chard, Kyle and Chard, Ryan and Foster, Ian and Lacinski, Lukasz and Papka, Michael E. and Wagner, Rick},
  title = {Petrel: A Programmatically Accessible Research Data Service},
  booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning)},
  year = {2019},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Pearc '19},
  articleno = {49},
  doi = {10.1145/3332186.3332241},
  isbn = {9781450372275},
  location = {Chicago, IL, USA},
  numpages = {7},
  url = {https://doi.org/10.1145/3332186.3332241},
}

2018

Li Yu, Zhou Zhou, Yuping Fan, Michael E. Papka, and Zhiling Lan, System-wide trade-off modeling of performance, power, and resilience on petascale systems, The Journal of Supercomputing, 2018
BibTeX
```
@article{YuZhFa18,
  author = {Yu, Li and Zhou, Zhou and Fan, Yuping and Papka, Michael E. and Lan, Zhiling},
  title = {System-wide trade-off modeling of performance, power, and resilience on petascale systems},
  journal = {The Journal of Supercomputing},
  year = {2018},
  volume = {74},
  number = {7},
  pages = {3168--3192},
  publisher = {Springer US},
}
```

Will Usher, Silvio Rizzi, Ingo Wald, Jefferson Amstutz, Joseph Insley, Venkatram Vishwanath, Nicola Ferrier, Michael E. Papka, and Valerio Pascucci, libIS: a lightweight library for flexible in transit visualization, In Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2018

BibTeX

@inproceedings{UsRiWa18,
  author = {Usher, Will and Rizzi, Silvio and Wald, Ingo and Amstutz, Jefferson and Insley, Joseph and Vishwanath, Venkatram and Ferrier, Nicola and Papka, Michael E. and Pascucci, Valerio},
  title = {libIS: a lightweight library for flexible in transit visualization},
  booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
  year = {2018},
  pages = {33--38},
}

Michael E. Papka, Jim Collins, Beth Cerny, and Nils Heinonen, 2018 Annual Report-Argonne Leadership Computing Facility, 2018
BibTeX
```
@techreport{PaCoCe18,
  author = {Papka, Michael E. and Collins, Jim and Cerny, Beth and Heinonen, Nils},
  title = {2018 Annual Report-Argonne Leadership Computing Facility},
  year = {2018},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```

Thomas Marrinan, Silvio Rizzi, Joseph Insley, Brian Toonen, William Allcock, and Michael E. Papka, Transferring data from high-performance simulations to extreme scale analysis applications in real-time, In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018

BibTeX

@inproceedings{MaRiIn18,
  author = {Marrinan, Thomas and Rizzi, Silvio and Insley, Joseph and Toonen, Brian and Allcock, William and Papka, Michael E.},
  title = {Transferring data from high-performance simulations to extreme scale analysis applications in real-time},
  booktitle = {2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2018},
  pages = {1214--1220},
  organization = {IEEE},
}

Preeti Malakar, Todd Munson, Christopher Knight, Venkatram Vishwanath, and Michael E. Papka, Topology-aware space-shared co-analysis of large-scale molecular dynamics simulations, In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, 2018

BibTeX

@inproceedings{MaMuKn18,
  author = {Malakar, Preeti and Munson, Todd and Knight, Christopher and Vishwanath, Venkatram and Papka, Michael E.},
  title = {Topology-aware space-shared co-analysis of large-scale molecular dynamics simulations},
  booktitle = {SC18: International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2018},
  pages = {305--319},
  organization = {IEEE},
}

Mark Kostuk, Thomas D. Uram, Todd Evans, Dmitri M. Orlov, Michael E. Papka, and David Schissel, Automatic between-pulse analysis of DIII-D experimental data performed remotely on a supercomputer at Argonne Leadership Computing Facility, Fusion Science and Technology, 2018

BibTeX

@article{KoUrEv18,
  author = {Kostuk, Mark and Uram, Thomas D. and Evans, Todd and Orlov, Dmitri M. and Papka, Michael E. and Schissel, David},
  title = {Automatic between-pulse analysis of DIII-D experimental data performed remotely on a supercomputer at Argonne Leadership Computing Facility},
  journal = {Fusion Science and Technology},
  year = {2018},
  volume = {74},
  number = {1-2},
  pages = {135--143},
  publisher = {Taylor & Francis},
}

Ryan Chard, Ravi Madduri, Nicholas T. Karonis, Kyle Chard, Kirk L. Duffin, Caesar E. Ordoñez, Thomas D. Uram, Justin Fleischauer, Ian T. Foster, Michael E. Papka, and John Winans, Scalable pCT Image Reconstruction Delivered as a Cloud Service, IEEE Transactions on Cloud Computing,Jan, 2018
Abstract BibTeX

We describe a cloud-based medical image reconstruction service designed to meet a real-time and daily demand to reconstruct thousands of images from proton cancer treatment facilities worldwide. Rapid reconstruction of a three-dimensional Proton Computed Tomography (pCT) image can require the transfer of 100 GB of data and use of approximately 120 GPU-enabled compute nodes. The nature of proton therapy means that demand for such a service is sporadic and comes from potentially hundreds of clients worldwide. We thus explore the use of a commercial cloud as a scalable and cost-efficient platform for pCT reconstruction. To address the high performance requirements of this application we leverage Amazon Web Services’ GPU-enabled cluster resources that are provisioned with high performance networks between nodes. To support episodic demand, we develop an on-demand multi-user provisioning service that can dynamically provision and resize clusters based on image reconstruction requirements, priorities, and wait times. We compare the performance of our pCT reconstruction service running on commercial cloud resources with that of the same application on dedicated local high performance computing resources. We show that we can achieve scalable and on-demand reconstruction of large scale pCT images for simultaneous multi-client requests, processing images in less than 10 minutes for less than $10 per image.
```
@article{ChMaKa18,
  author = {Chard, Ryan and Madduri, Ravi and Karonis, Nicholas T. and Chard, Kyle and Duffin, Kirk L. and Ordo{\~n}ez, Caesar E. and Uram, Thomas D. and Fleischauer, Justin and Foster, Ian T. and Papka, Michael E. and Winans, John},
  title = {Scalable pCT Image Reconstruction Delivered as a Cloud Service},
  journal = {IEEE Transactions on Cloud Computing},
  year = {2018},
  month = jan,
  volume = {6},
  number = {01},
  pages = {182--195},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  doi = {10.1109/tcc.2015.2457423},
  issn = {2168-7161},
  keywords = {image reconstruction;protons;history;cloud computing;medical treatment;biomedical imaging},
}
```

Bennett Bernardoni, Nicola Ferrier, Joseph Insley, Michael E. Papka, Saumil Patel, and Silvio Rizzi, In situ visualization and analysis to design large scale experiments in computational fluid dynamics, In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), 2018

BibTeX

@inproceedings{BeFeIn18,
  author = {Bernardoni, Bennett and Ferrier, Nicola and Insley, Joseph and Papka, Michael E. and Patel, Saumil and Rizzi, Silvio},
  title = {In situ visualization and analysis to design large scale experiments in computational fluid dynamics},
  booktitle = {2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2018},
  pages = {94--95},
  organization = {IEEE},
}

William Allcock, Bennett Bernardoni, Colleen Bertoni, Neil Getty, Joseph Insley, Michael E. Papka, Silvio Rizzi, and Brian Toonen, Ram as a network managed resource, In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018
BibTeX
```
@inproceedings{AlBeBe18,
  author = {Allcock, William and Bernardoni, Bennett and Bertoni, Colleen and Getty, Neil and Insley, Joseph and Papka, Michael E. and Rizzi, Silvio and Toonen, Brian},
  title = {Ram as a network managed resource},
  booktitle = {2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2018},
  pages = {99--106},
  organization = {IEEE},
}
```
Joseph Adamo, J. D. Emberson, Edouard Brooks, Silvio Rizzi, Joseph Insley, and Michael E. Papka, Exploring visualization techniques with hacc simulation data, In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), 2018
BibTeX
```
@inproceedings{AdEmBr18,
  author = {Adamo, Joseph and Emberson, J. D. and Brooks, Edouard and Rizzi, Silvio and Insley, Joseph and Papka, Michael E.},
  title = {Exploring visualization techniques with hacc simulation data},
  booktitle = {2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2018},
  pages = {92--93},
  organization = {IEEE},
}
```

2017

Sarah Read, and Michael E. Papka, Operational metrics reporting processes at scientific user facilities: Comparing a high-energy x-ray synchrotron facility to a supercomputing facility, In 2017 IEEE International Professional Communication Conference (ProComm), 2017
BibTeX
```
@inproceedings{RePa17,
  author = {Read, Sarah and Papka, Michael E.},
  title = {Operational metrics reporting processes at scientific user facilities: Comparing a high-energy x-ray synchrotron facility to a supercomputing facility},
  booktitle = {2017 IEEE International Professional Communication Conference (ProComm)},
  year = {2017},
  pages = {1--6},
  organization = {IEEE},
}
```

Preeti Malakar, Venkatram Vishwanath, Christopher Knight, Todd Munson, and Michael Papka, Scalable in situ analysis for large-scale molecular dynamics simulations on supercomputers, In Abstracts Of Papers Of The American Chemical Society, 2017

BibTeX

@inproceedings{MaViKn17,
  author = {Malakar, Preeti and Vishwanath, Venkatram and Knight, Christopher and Munson, Todd and Papka, Michael},
  title = {Scalable in situ analysis for large-scale molecular dynamics simulations on supercomputers},
  booktitle = {Abstracts Of Papers Of The American Chemical Society},
  year = {2017},
  volume = {254},
  organization = {Amer Chemical Soc 1155 16th St, Nw, Washington, DC 20036 Usa},
}

Preeti Malakar, Christopher Knight, Todd Munson, Venkatram Vishwanath, and Michael E. Papka, Scalable In Situ Analysis of Molecular Dynamics Simulations, In Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, 2017

Abstract BibTeX

Analysis of scientific simulation data enables scientists to glean insight from simulations. In situ analysis, which can be simultaneously executed with the simulation, mitigates I/O bottlenecks and can accelerate discovery of new phenomena. However, in typical modes of operation, this requires either stalling simulation during analysis phase or transferring data for analysis. We study the scalability challenges of time- and space-shared modes of analyzing large-scale molecular dynamics simulations. We also propose topology-aware mapping for simulation and analysis. We demonstrate the benefits of our approach using LAMMPS code on two supercomputers.

@incollection{MaKnMu17,
  author = {Malakar, Preeti and Knight, Christopher and Munson, Todd and Vishwanath, Venkatram and Papka, Michael E.},
  title = {Scalable In Situ Analysis of Molecular Dynamics Simulations},
  booktitle = {Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization},
  year = {2017},
  pages = {1–6},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Isav'17},
  doi = {10.1145/3144769.3144772},
  isbn = {9781450351393},
  location = {Denver, CO, USA},
  numpages = {6},
  url = {https://doi.org/10.1145/3144769.3144772},
}

Thomas Marrinan, Joseph A. Insley, Silvio Rizzi, François Tessier, and Michael E. Papka, Automated dynamic data redistribution, In 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017
BibTeX
```
@inproceedings{MaInRi17,
  author = {Marrinan, Thomas and Insley, Joseph A. and Rizzi, Silvio and Tessier, Fran{\c{c}}ois and Papka, Michael E.},
  title = {Automated dynamic data redistribution},
  booktitle = {2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2017},
  pages = {1208--1215},
  organization = {IEEE},
}
```
Michael J. Lewis, George K. Thiruvathukal, Venkatram Vishwanath, Michael E. Papka, and Andrew Johnson, A distributed graph approach for pre-processing linked RDF data using supercomputers, In Proceedings of The International Workshop on Semantic Big Data, 2017
BibTeX
```
@inproceedings{LeThVi17,
  author = {Lewis, Michael J. and Thiruvathukal, George K. and Vishwanath, Venkatram and Papka, Michael E. and Johnson, Andrew},
  title = {A distributed graph approach for pre-processing linked RDF data using supercomputers},
  booktitle = {Proceedings of The International Workshop on Semantic Big Data},
  year = {2017},
  pages = {1--6},
}
```
Bharat Kale, Harish Varma Siravuri, Hamed Alhoori, and Michael E. Papka, Predicting research that will be cited in policy documents, In Proceedings of the 2017 ACM on Web Science Conference, 2017
BibTeX
```
@inproceedings{KaSiAl17,
  author = {Kale, Bharat and Siravuri, Harish Varma and Alhoori, Hamed and Papka, Michael E.},
  title = {Predicting research that will be cited in policy documents},
  booktitle = {Proceedings of the 2017 ACM on Web Science Conference},
  year = {2017},
  pages = {389--390},
}
```
Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael E. Papka, and Kwan-Liu Ma, A visual analytics system for optimizing communications in massively parallel applications, In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST), 2017
BibTeX
```
@inproceedings{FuMaRe17,
  author = {Fujiwara, Takanori and Malakar, Preeti and Reda, Khairi and Vishwanath, Venkatram and Papka, Michael E. and Ma, Kwan-Liu},
  title = {A visual analytics system for optimizing communications in massively parallel applications},
  booktitle = {2017 IEEE Conference on Visual Analytics Science and Technology (VAST)},
  year = {2017},
  pages = {59--70},
  organization = {IEEE},
}
```
Tommy Franczak, Asare Nkansah, Thomas Marrinan, and Michael E. Papka, A path from serial execution to hybrid parallelization for learning hpc, In EduHPC-17: Workshop on Education for High-Performance Computing, 2017
BibTeX
```
@inproceedings{FrNkMa17,
  author = {Franczak, Tommy and Nkansah, Asare and Marrinan, Thomas and Papka, Michael E.},
  title = {A path from serial execution to hybrid parallelization for learning hpc},
  booktitle = {EduHPC-17: Workshop on Education for High-Performance Computing},
  year = {2017},
  volume = {17},
}
```
Yuping Fan, Paul Rich, William E. Allcock, Michael E. Papka, and Zhiling Lan, Trade-off between prediction accuracy and underestimation rate in job runtime estimates, In 2017 IEEE International Conference on Cluster Computing (CLUSTER), 2017
BibTeX
```
@inproceedings{FaRiAl17,
  author = {Fan, Yuping and Rich, Paul and Allcock, William E. and Papka, Michael E. and Lan, Zhiling},
  title = {Trade-off between prediction accuracy and underestimation rate in job runtime estimates},
  booktitle = {2017 IEEE International Conference on Cluster Computing (CLUSTER)},
  year = {2017},
  pages = {530--540},
  organization = {IEEE},
}
```
John T. Childers, Thomas D. Uram, Thomas J. LeCompte, Michael E. Papka, and Douglas P. Benjamin, Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads, Computer Physics Communications, 2017
Abstract BibTeX

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was targeted for adaptation to run on Mira, the supercomputer at the Argonne Leadership Computing Facility. Alpgen is a Monte Carlo event generation application that is used by LHC experiments in the simulation of collisions that take place in the Large Hadron Collider. This paper details the process by which Alpgen was adapted from a single-processor serial-application to a large-scale parallel-application and the performance that was achieved.
```
@article{ChUrLe17,
  author = {Childers, John T. and Uram, Thomas D. and LeCompte, Thomas J. and Papka, Michael E. and Benjamin, Douglas P.},
  title = {{Adapting the serial Alpgen parton-interaction generator to simulate LHC collisions on millions of parallel threads}},
  journal = {Computer Physics Communications},
  year = {2017},
  volume = {210},
  pages = {54--59},
  publisher = {North-Holland},
  doi = {https://doi.org/10.1016/j.cpc.2016.09.013},
  issn = {0010-4655},
  keywords = {Supercomputer, HEP, Simulation, Parallel},
  url = {https://www.sciencedirect.com/science/article/pii/S0010465516302843},
}
```
J Taylor Childers, Thomas D. Uram, Doug Benjamin, Thomas J. LeCompte, and Michael E. Papka, An edge service for managing HPC workflows, In Proceedings of the Fourth International Workshop on HPC User Support Tools, 2017
BibTeX
```
@inproceedings{ChUrBe17,
  author = {Childers, J Taylor and Uram, Thomas D. and Benjamin, Doug and LeCompte, Thomas J. and Papka, Michael E.},
  title = {An edge service for managing HPC workflows},
  booktitle = {Proceedings of the Fourth International Workshop on HPC User Support Tools},
  year = {2017},
  pages = {1--8},
}
```
Edouard B. Brooks, Joseph Insley, Michael E. Papka, and Silvio Rizzi, Virtual reality tools for the correction of automated volume segmentation errors using dense surface reconstructions, In 2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV), 2017
BibTeX
```
@inproceedings{BrInPa17,
  author = {Brooks, Edouard B. and Insley, Joseph and Papka, Michael E. and Rizzi, Silvio},
  title = {Virtual reality tools for the correction of automated volume segmentation errors using dense surface reconstructions},
  booktitle = {2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2017},
  pages = {92--93},
  organization = {IEEE},
}
```
Christian Bailey, Bharat Kale, Jamieson Walker, Harish Varma Siravuri, Hamed Alhoori, and Michael E. Papka, Exploring features for predicting policy citations, In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2017
BibTeX
```
@inproceedings{BaKaWa17,
  author = {Bailey, Christian and Kale, Bharat and Walker, Jamieson and Siravuri, Harish Varma and Alhoori, Hamed and Papka, Michael E.},
  title = {Exploring features for predicting policy citations},
  booktitle = {2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
  year = {2017},
  pages = {1--2},
  organization = {IEEE},
}
```

2016

Dawid Zawislak, Brian Toonen, William Allcock, Silvio Rizzi, Joseph Insley, Venkatram Vishwanath, and Michael E. Papka, Early Investigations into using a Remote RAM Pool with the vl3 Visualization Framework, In 2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV), 2016

BibTeX

@inproceedings{ZaToAl16,
  author = {Zawislak, Dawid and Toonen, Brian and Allcock, William and Rizzi, Silvio and Insley, Joseph and Vishwanath, Venkatram and Papka, Michael E.},
  title = {{Early Investigations into using a Remote RAM Pool with the vl3 Visualization Framework}},
  booktitle = {2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)},
  year = {2016},
  pages = {23--28},
  organization = {IEEE},
}

Sean Wallace, Zhou Zhou, Venkatram Vishwanath, Susan Coghlan, John Tramm, Zhiling Lan, and Michael E. Papka, Application power profiling on IBM Blue Gene/Q, Parallel Computing, 2016
BibTeX
```
@article{WaZhVi16,
  author = {Wallace, Sean and Zhou, Zhou and Vishwanath, Venkatram and Coghlan, Susan and Tramm, John and Lan, Zhiling and Papka, Michael E.},
  title = {Application power profiling on IBM Blue Gene/Q},
  journal = {Parallel Computing},
  year = {2016},
  volume = {57},
  pages = {73--86},
  publisher = {North-Holland},
}
```

Sean Wallace, Xu Yang, Venkatram Vishwanath, William E. Allcock, Susan Coghlan, Michael E. Papka, and Zhiling Lan, A data driven scheduling approach for power management on hpc systems, In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2016

BibTeX

@inproceedings{WaYaVi16,
  author = {Wallace, Sean and Yang, Xu and Vishwanath, Venkatram and Allcock, William E. and Coghlan, Susan and Papka, Michael E. and Lan, Zhiling},
  title = {A data driven scheduling approach for power management on hpc systems},
  booktitle = {SC'16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2016},
  pages = {656--666},
  organization = {IEEE},
}

Will Usher, Ingo Wald, Aaron Knoll, Michael E. Papka, and Valerio Pascucci, In Situ Exploration of Particle Simulations with CPU Ray Tracing, Supercomputing Frontiers and Innovations,Oct, 2016
Abstract BibTeX

We present a system for interactive in situ visualization of large particle simulations, suitable for general CPU-based HPC architectures. As simulations grow in scale, in situ methods are needed to alleviate IO bottlenecks and visualize data at full spatio-temporal resolution. We use a lightweight loosely-coupled layer serving distributed data from the simulation to a data-parallel renderer running in separate processes. Leveraging the OSPRay ray tracing framework for visualization and balanced P-k-d trees, we can render simulation data in real-time, as they arrive, with negligible memory overhead. This flexible solution allows users to perform exploratory in situ visualization on the same computational resources as the simulation code, on dedicated visualization clusters or remote workstations, via a standalone rendering client that can be connected or disconnected as needed. We evaluate this system on simulations with up to 227M particles in the LAMMPS and Uintah computational frameworks, and show that our approach provides many of the advantages of tightly-coupled systems, with the flexibility to render on a wide variety of remote and coprocessing resources.<br /><br />
```
@article{UsWaKn16,
  author = {Usher, Will and Wald, Ingo and Knoll, Aaron and Papka, Michael E. and Pascucci, Valerio},
  title = {In Situ Exploration of Particle Simulations with CPU Ray Tracing},
  journal = {Supercomputing Frontiers and Innovations},
  year = {2016},
  month = oct,
  volume = {3},
  number = {4},
  pages = {4--18},
  doi = {10.14529/jsfi160401},
  url = {https://superfri.org/index.php/superfri/article/view/112},
  bdsk-url-1 = {https://superfri.org/index.php/superfri/article/view/112},
  bdsk-url-2 = {https://doi.org/10.14529/jsfi160401}
}
```
Thomas D. Uram, and Michael E. Papka, Expanding the Scope of High-Performance Computing Facilities, Computing in Science & Engineering, 2016
BibTeX
```
@article{UrPa16,
  author = {Uram, Thomas D. and Papka, Michael E.},
  title = {Expanding the Scope of High-Performance Computing Facilities},
  journal = {Computing in Science & Engineering},
  year = {2016},
  volume = {18},
  number = {3},
  pages = {84--87},
  publisher = {IEEE},
}
```

Min Shih, Silvio Rizzi, Joseph Insley, Thomas Uram, Venkatram Vishwanath, Mark Hereld, Michael E. Papka, and Kwan-Liu Ma, Parallel distributed, gpu-accelerated, advanced lighting calculations for large-scale volume visualization, In 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), 2016

BibTeX

@inproceedings{ShRiIn16,
  author = {Shih, Min and Rizzi, Silvio and Insley, Joseph and Uram, Thomas and Vishwanath, Venkatram and Hereld, Mark and Papka, Michael E. and Ma, Kwan-Liu},
  title = {Parallel distributed, gpu-accelerated, advanced lighting calculations for large-scale volume visualization},
  booktitle = {2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2016},
  pages = {47--55},
  organization = {IEEE},
}

Silvio Rizzi, Mark Hereld, Joseph Insley, Preeti Malakar, Michael E. Papka, Thomas Uram, and Venkatram Vishwanath, Coupling lammps and the vl3 framework for co-visualization of atomistic simulations, In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016

BibTeX

@inproceedings{RiHeIn16,
  author = {Rizzi, Silvio and Hereld, Mark and Insley, Joseph and Malakar, Preeti and Papka, Michael E. and Uram, Thomas and Vishwanath, Venkatram},
  title = {Coupling lammps and the vl3 framework for co-visualization of atomistic simulations},
  booktitle = {2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2016},
  pages = {1038--1042},
  organization = {IEEE},
}

Sarah Read, and Michael E. Papka, Improving models of document cycling: Accounting for the less visible writing activities of an annual reporting process at a supercomputing facility, In 2016 IEEE International Professional Communication Conference (IPCC), 2016
BibTeX
```
@inproceedings{RePa16,
  author = {Read, Sarah and Papka, Michael E.},
  title = {Improving models of document cycling: Accounting for the less visible writing activities of an annual reporting process at a supercomputing facility},
  booktitle = {2016 IEEE International Professional Communication Conference (IPCC)},
  year = {2016},
  pages = {1--10},
  organization = {IEEE},
}
```
Khairi Reda, Andrew E. Johnson, Michael E. Papka, and Jason Leigh, Modeling and evaluating user behavior in exploratory visual analysis, Information Visualization, 2016
BibTeX
```
@article{ReJoPa16,
  author = {Reda, Khairi and Johnson, Andrew E. and Papka, Michael E. and Leigh, Jason},
  title = {Modeling and evaluating user behavior in exploratory visual analysis},
  journal = {Information Visualization},
  year = {2016},
  volume = {15},
  number = {4},
  pages = {325--339},
  publisher = {SAGE Publications Sage UK: London, England},
}
```
Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, and George Em Karniadakis, Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms, Parallel Computing, 2016
BibTeX
```
@article{PeInGr16,
  author = {Perdikaris, Paris and Insley, Joseph A. and Grinberg, Leopold and Yu, Yue and Papka, Michael E. and Karniadakis, George Em},
  title = {Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms},
  journal = {Parallel Computing},
  year = {2016},
  volume = {55},
  pages = {9--16},
  publisher = {North-Holland},
}
```
Michael Papka, LSPP 2016 Keynote, In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016
BibTeX
```
@inproceedings{Pa16,
  author = {Papka, Michael},
  title = {LSPP 2016 Keynote},
  booktitle = {2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2016},
  pages = {1340--1340},
  organization = {IEEE},
}
```

Preeti Malakar, Venkatram Vishwanath, Christopher Knight, Todd Munson, and Michael E. Papka, Optimal execution of co-analysis for large-scale molecular dynamics simulations, In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2016

BibTeX

@inproceedings{MaViKn16,
  author = {Malakar, Preeti and Vishwanath, Venkatram and Knight, Christopher and Munson, Todd and Papka, Michael E.},
  title = {Optimal execution of co-analysis for large-scale molecular dynamics simulations},
  booktitle = {SC'16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2016},
  pages = {702--715},
  organization = {IEEE},
}

Thomas Marrinan, Arthur Nishimoto, Joseph A. Insley, Silvio Rizzi, Andrew Johnson, and Michael E. Papka, Interactive multi-modal display spaces for visual analysis, In Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces, 2016
BibTeX
```
@inproceedings{MaNiIn16,
  author = {Marrinan, Thomas and Nishimoto, Arthur and Insley, Joseph A. and Rizzi, Silvio and Johnson, Andrew and Papka, Michael E.},
  title = {Interactive multi-modal display spaces for visual analysis},
  booktitle = {Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces},
  year = {2016},
  pages = {421--426},
}
```
Si Liu, Eun-Sung Jung, Rajkumar Kettimuthu, Xian-He Sun, and Michael Papka, Towards optimizing large-scale data transfers with end-to-end integrity verification, In 2016 IEEE International Conference on Big Data (Big Data), 2016
BibTeX
```
@inproceedings{LiJuKe16,
  author = {Liu, Si and Jung, Eun-Sung and Kettimuthu, Rajkumar and Sun, Xian-He and Papka, Michael},
  title = {Towards optimizing large-scale data transfers with end-to-end integrity verification},
  booktitle = {2016 IEEE International Conference on Big Data (Big Data)},
  year = {2016},
  pages = {3002--3007},
  organization = {IEEE},
}
```
Jim Collins, Michael E. Papka, Beth A. Cerny, and Richard M. Coffey, 2016 Annual Report-Argonne Leadership Computing Facility, 2016
BibTeX
```
@techreport{CoPaCe16,
  author = {Collins, Jim and Papka, Michael E. and Cerny, Beth A. and Coffey, Richard M.},
  title = {2016 Annual Report-Argonne Leadership Computing Facility},
  year = {2016},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```
James R. Collins, Beth A. Cerny, Laura Wolf, Richard M. Coffey, and Michael E. Papka, 2016 ALCF Science Highlights, 2016
BibTeX
```
@techreport{CoCeWo16,
  author = {Collins, James R. and Cerny, Beth A. and Wolf, Laura and Coffey, Richard M. and Papka, Michael E.},
  title = {2016 ALCF Science Highlights},
  year = {2016},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```
Huy Bui, Eun-Sung Jung, Venkatram Vishwanath, Andrew Johnson, Jason Leigh, and Michael E. Papka, Improving sparse data movement performance using multiple paths on the Blue Gene/Q supercomputer, Parallel Computing, 2016
BibTeX
```
@article{BuJuVi16,
  author = {Bui, Huy and Jung, Eun-Sung and Vishwanath, Venkatram and Johnson, Andrew and Leigh, Jason and Papka, Michael E.},
  title = {Improving sparse data movement performance using multiple paths on the Blue Gene/Q supercomputer},
  journal = {Parallel Computing},
  year = {2016},
  volume = {51},
  pages = {3--16},
  publisher = {North-Holland},
}
```
Pete Beckman, Rajesh Sankaran, Charlie Catlett, Nicola Ferrier, Robert Jacob, and Michael E. Papka, Waggle: An Open Sensor Platform for Edge Computing, In 2016 IEEE Sensors, 2016
BibTeX
```
@inproceedings{BeSaCa16,
  author = {Beckman, Pete and Sankaran, Rajesh and Catlett, Charlie and Ferrier, Nicola and Jacob, Robert and Papka, Michael E.},
  title = {Waggle: An Open Sensor Platform for Edge Computing},
  booktitle = {2016 IEEE Sensors},
  year = {2016},
  pages = {1--3},
  organization = {IEEE},
}
```

2015

Li Yu, Zhou Zhou, Sean Wallace, Michael E. Papka, and Zhiling Lan, Quantitative modeling of power performance tradeoffs on extreme scale systems, Journal of Parallel and Distributed Computing, 2015
BibTeX
```
@article{YuZhWa15,
  author = {Yu, Li and Zhou, Zhou and Wallace, Sean and Papka, Michael E. and Lan, Zhiling},
  title = {Quantitative modeling of power performance tradeoffs on extreme scale systems},
  journal = {Journal of Parallel and Distributed Computing},
  year = {2015},
  volume = {84},
  pages = {1--14},
  publisher = {Academic Press},
}
```
Sean Wallace, Venkatram Vishwanath, Susan Coghlan, Zhiling Lan, and Michael E. Papka, Comparison of vendor supplied environmental data collection mechanisms, In 2015 IEEE International Conference on Cluster Computing, 2015
BibTeX
```
@inproceedings{WaViCo15,
  author = {Wallace, Sean and Vishwanath, Venkatram and Coghlan, Susan and Lan, Zhiling and Papka, Michael E.},
  booktitle = {2015 IEEE International Conference on Cluster Computing},
  year = {2015},
  pages = {690--697},
  organization = {IEEE},
}
```
Ingo Wald, Aaron Knoll, Gregory P. Johnson, Will Usher, Valerio Pascucci, and Michael E. Papka, CPU ray tracing large particle data with balanced Pkd trees, In 2015 IEEE Scientific Visualization Conference (SciVis), 2015
BibTeX
```
@inproceedings{WaKnJo15,
  author = {Wald, Ingo and Knoll, Aaron and Johnson, Gregory P. and Usher, Will and Pascucci, Valerio and Papka, Michael E.},
  title = {CPU ray tracing large particle data with balanced Pkd trees},
  booktitle = {2015 IEEE Scientific Visualization Conference (SciVis)},
  year = {2015},
  pages = {57--64},
  organization = {IEEE},
}
```

Thomas D. Uram, J. Taylor Childers, Thomas J. LeCompte, Michael E. Papka, and Doug Benjamin, Achieving production-level use of HEP software at the Argonne Leadership Computing Facility, In Journal of Physics: Conference Series, 2015

BibTeX

@inproceedings{UrChLe15,
  author = {Uram, Thomas D. and Childers, J. Taylor and LeCompte, Thomas J. and Papka, Michael E. and Benjamin, Doug},
  title = {Achieving production-level use of HEP software at the Argonne Leadership Computing Facility},
  booktitle = {Journal of Physics: Conference Series},
  year = {2015},
  volume = {664},
  number = {6},
  pages = {062063},
  organization = {IOP Publishing},
}

Silvio Rizzi, Mark Hereld, Joseph Insley, Michael E. Papka, Thomas Uram, and Venkatram Vishwanath, Large-scale parallel visualization of particle-based simulations using point sprites and level-of-detail, In Proceedings of the 15th Eurographics Symposium on Parallel Graphics and Visualization, 2015
BibTeX
```
@inproceedings{RiHeIn15b,
  author = {Rizzi, Silvio and Hereld, Mark and Insley, Joseph and Papka, Michael E. and Uram, Thomas and Vishwanath, Venkatram},
  title = {Large-scale parallel visualization of particle-based simulations using point sprites and level-of-detail},
  booktitle = {Proceedings of the 15th Eurographics Symposium on Parallel Graphics and Visualization},
  year = {2015},
  pages = {1--10},
}
```
Silvio Rizzi, Mark Hereld, Joseph Insley, Michael E. Papka, Thomas Uram, and Venkatram Vishwanath, Large-scale co-visualization for LAMMPS using vl3, In 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), 2015
BibTeX
```
@inproceedings{RiHeIn15a,
  author = {Rizzi, Silvio and Hereld, Mark and Insley, Joseph and Papka, Michael E. and Uram, Thomas and Vishwanath, Venkatram},
  title = {Large-scale co-visualization for LAMMPS using vl3},
  booktitle = {2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2015},
  pages = {141--142},
  organization = {IEEE},
}
```
Khairi Reda, Andrew E. Johnson, Michael E. Papka, and Jason Leigh, Effects of display size and resolution on user behavior and insight acquisition in visual exploration, In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015
BibTeX
```
@inproceedings{ReJoPa15,
  author = {Reda, Khairi and Johnson, Andrew E. and Papka, Michael E. and Leigh, Jason},
  title = {Effects of display size and resolution on user behavior and insight acquisition in visual exploration},
  booktitle = {Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems},
  year = {2015},
  pages = {2759--2768},
}
```
Khairi Reda, Alberto González, Jason Leigh, and Michael E. Papka, Tell me what do you see: Detecting perceptually-separable visual patterns via clustering of image-space features in visualizations, In 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), 2015
BibTeX
```
@inproceedings{ReGoLe15,
  author = {Reda, Khairi and Gonz{\'a}lez, Alberto and Leigh, Jason and Papka, Michael E.},
  title = {Tell me what do you see: Detecting perceptually-separable visual patterns via clustering of image-space features in visualizations},
  booktitle = {2015 IEEE Conference on Visual Analytics Science and Technology (VAST)},
  year = {2015},
  pages = {211--212},
  organization = {IEEE},
}
```

Preeti Malakar, Venkatram Vishwanath, Todd Munson, Christopher Knight, Mark Hereld, Sven Leyffer, and Michael E. Papka, Optimal scheduling of in-situ analysis for large-scale scientific simulations, In SC’15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2015

BibTeX

@inproceedings{MaViMu15,
  author = {Malakar, Preeti and Vishwanath, Venkatram and Munson, Todd and Knight, Christopher and Hereld, Mark and Leyffer, Sven and Papka, Michael E.},
  title = {Optimal scheduling of in-situ analysis for large-scale scientific simulations},
  booktitle = {SC'15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2015},
  pages = {1--11},
  organization = {IEEE},
}

Jie Jiang, Mark Hereld, Joseph Insley, Michael E. Papka, Silvio Rizzi, Thomas Uram, and Venkatram Vishwanath, Streaming ultra high resolution images to large tiled display at nearly interactive frame rate with vl3, In 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), 2015

BibTeX

@inproceedings{JiHeIn15,
  author = {Jiang, Jie and Hereld, Mark and Insley, Joseph and Papka, Michael E. and Rizzi, Silvio and Uram, Thomas and Vishwanath, Venkatram},
  title = {Streaming ultra high resolution images to large tiled display at nearly interactive frame rate with vl3},
  booktitle = {2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV)},
  year = {2015},
  pages = {133--134},
  organization = {IEEE},
}

James J. Hack, and Michael E. Papka, Big data: Next-generation machines for big science, Computing in Science & Engineering, 2015
BibTeX
```
@article{HaPa15,
  author = {Hack, James J. and Papka, Michael E.},
  title = {Big data: Next-generation machines for big science},
  journal = {Computing in Science & Engineering},
  year = {2015},
  volume = {17},
  number = {4},
  pages = {63--65},
  publisher = {IEEE},
}
```

Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss, and Valerio Pascucci, Interstitial and interlayer ion diffusion geometry extraction in graphitic nanosphere battery materials, IEEE transactions on visualization and computer graphics, 2015

BibTeX

@article{GyKnLa15b,
  author = {Gyulassy, Attila and Knoll, Aaron and Lau, Kah Chun and Wang, Bei and Bremer, Peer-Timo and Papka, Michael E. and Curtiss, Larry A. and Pascucci, Valerio},
  title = {Interstitial and interlayer ion diffusion geometry extraction in graphitic nanosphere battery materials},
  journal = {IEEE transactions on visualization and computer graphics},
  year = {2015},
  volume = {22},
  number = {1},
  pages = {916--925},
  publisher = {IEEE},
}

Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss, and Valerio Pascucci, Morse-smale analysis of ion diffusion in ab initio battery materials simulations, In Topological Methods in Data Analysis and Visualization, 2015

BibTeX

@inproceedings{GyKnLa15a,
  author = {Gyulassy, Attila and Knoll, Aaron and Lau, Kah Chun and Wang, Bei and Bremer, Peer-Timo and Papka, Michael E. and Curtiss, Larry A. and Pascucci, Valerio},
  title = {Morse-smale analysis of ion diffusion in ab initio battery materials simulations},
  booktitle = {Topological Methods in Data Analysis and Visualization},
  year = {2015},
  pages = {135--149},
  organization = {Springer, Cham},
}

J. Taylor Childers, Thomas D. Uram, Thomas J. LeCompte, Michael E. Papka, and Douglas P. Benjamin, Simulation of LHC events on a millions threads, In Journal of Physics: Conference Series, 2015
BibTeX
```
@inproceedings{ChUrLe15,
  author = {Childers, J. Taylor and Uram, Thomas D. and LeCompte, Thomas J. and Papka, Michael E. and Benjamin, Douglas P.},
  title = {Simulation of LHC events on a millions threads},
  booktitle = {Journal of Physics: Conference Series},
  year = {2015},
  volume = {664},
  number = {9},
  pages = {092006},
  organization = {IOP Publishing},
}
```

Huy Bui, Preeti Malakar, Venkatram Vishwanath, Todd S. Munson, Eun-Sung Jung, Andrew Johnson, Michael E. Papka, and Jason Leigh, Improving communication throughput by multipath load balancing on Blue Gene/Q, In 2015 IEEE 22nd International Conference on High Performance Computing (HiPC), 2015

BibTeX

@inproceedings{BuMaVi15,
  author = {Bui, Huy and Malakar, Preeti and Vishwanath, Venkatram and Munson, Todd S. and Jung, Eun-Sung and Johnson, Andrew and Papka, Michael E. and Leigh, Jason},
  title = {Improving communication throughput by multipath load balancing on Blue Gene/Q},
  booktitle = {2015 IEEE 22nd International Conference on High Performance Computing (HiPC)},
  year = {2015},
  pages = {115--124},
  organization = {IEEE},
}

Huy Bui, Robert Jacob, Preeti Malakar, Venkatram Viswanath, Andrew Johnson, Micheal E. Papka, and Jason Leigh, Multipath Load Balancing for M\times N Communication Patterns on the Blue Gene/Q Supercomputer Interconnection Network, In 2015 IEEE International Conference on Cluster Computing, 2015

BibTeX

@inproceedings{BuJaMa15,
  author = {Bui, Huy and Jacob, Robert and Malakar, Preeti and Viswanath, Venkatram and Johnson, Andrew and Papka, Micheal E. and Leigh, Jason},
  title = {Multipath Load Balancing for M$\times$ N Communication Patterns on the Blue Gene/Q Supercomputer Interconnection Network},
  booktitle = {2015 IEEE International Conference on Cluster Computing},
  year = {2015},
  pages = {833--840},
  organization = {IEEE},
}

2014

Venkatram Vishwanath, Huy Bui, Mark Hereld, and Michael E. Papka, Glean, In High Performance Parallel I/O, 2014
BibTeX
```
@incollection{ViBuHe14,
  author = {Vishwanath, Venkatram and Bui, Huy and Hereld, Mark and Papka, Michael E.},
  title = {Glean},
  booktitle = {High Performance Parallel I/O},
  year = {2014},
  pages = {257--266},
  publisher = {Chapman and Hall/CRC},
}
```
Silvio Rizzi, Mark Hereld, Joseph Insley, Michael E. Papka, Thomas Uram, and Venkatram Vishwanath, Performance modeling of vl3 volume rendering on GPU-based clusters, In Proceedings of the 14th Eurographics Symposium on Parallel Graphics and Visualization, 2014
BibTeX
```
@inproceedings{RiHeIn14,
  author = {Rizzi, Silvio and Hereld, Mark and Insley, Joseph and Papka, Michael E. and Uram, Thomas and Vishwanath, Venkatram},
  title = {Performance modeling of vl3 volume rendering on GPU-based clusters},
  booktitle = {Proceedings of the 14th Eurographics Symposium on Parallel Graphics and Visualization},
  year = {2014},
  pages = {65--72},
}
```
Sarah Read, and Michael E. Papka, Genre cycling: The infrastructural function of an operational assessment review and reporting process at a federal scientific supercomputing user facility, In Proceedings of the 32nd ACM International Conference on The Design of Communication CD-ROM, 2014
BibTeX
```
@inproceedings{RePa14,
  author = {Read, Sarah and Papka, Michael E.},
  title = {Genre cycling: The infrastructural function of an operational assessment review and reporting process at a federal scientific supercomputing user facility},
  booktitle = {Proceedings of the 32nd ACM International Conference on The Design of Communication CD-ROM},
  year = {2014},
  pages = {1--8},
}
```
Khairi Reda, Andrew E. Johnson, Jason Leigh, and Michael E. Papka, Evaluating user behavior and strategy during visual exploration, In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, 2014
BibTeX
```
@inproceedings{ReJoLe14,
  author = {Reda, Khairi and Johnson, Andrew E. and Leigh, Jason and Papka, Michael E.},
  title = {Evaluating user behavior and strategy during visual exploration},
  booktitle = {Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization},
  year = {2014},
  pages = {41--45},
}
```
Phil Lindner, Adolfo Rodriguez, Thomas D. Uram, Michael E. Papka, and Michael Papka, Augmenting views on large format displays with tablets, In Proceedings of the 2nd ACM symposium on Spatial user interaction, 2014
BibTeX
```
@inproceedings{LiRoUr14,
  author = {Lindner, Phil and Rodriguez, Adolfo and Uram, Thomas D. and Papka, Michael E. and Papka, Michael},
  title = {Augmenting views on large format displays with tablets},
  booktitle = {Proceedings of the 2nd ACM symposium on Spatial user interaction},
  year = {2014},
  pages = {161--161},
}
```

Sriram Lakshminarasimhan, Xiaocheng Zou, David A. Boyuka, Saurabh V. Pendse, John Jenkins, Venkatram Vishwanath, Michael E. Papka, Scott Klasky, and Nagiza F. Samatova, DIRAQ: scalable in situ data-and resource-aware indexing for optimized query performance, Cluster computing, 2014

BibTeX

@article{LaZoBo14,
  author = {Lakshminarasimhan, Sriram and Zou, Xiaocheng and Boyuka, David A. and Pendse, Saurabh V. and Jenkins, John and Vishwanath, Venkatram and Papka, Michael E. and Klasky, Scott and Samatova, Nagiza F.},
  title = {DIRAQ: scalable in situ data-and resource-aware indexing for optimized query performance},
  journal = {Cluster computing},
  year = {2014},
  volume = {17},
  number = {4},
  pages = {1101--1119},
  publisher = {Springer US},
}

Aaron Knoll, Ingo Wald, Paul Navratil, Anne Bowen, Khairi Reda, Michael E. Papka, and Kelly Gaither, RBF volume ray casting on multicore and manycore CPUs, In Computer Graphics Forum, 2014
BibTeX
```
@inproceedings{KnWaNa14,
  author = {Knoll, Aaron and Wald, Ingo and Navratil, Paul and Bowen, Anne and Reda, Khairi and Papka, Michael E. and Gaither, Kelly},
  title = {RBF volume ray casting on multicore and manycore CPUs},
  booktitle = {Computer Graphics Forum},
  year = {2014},
  volume = {33},
  number = {3},
  pages = {71--80},
}
```
James J. Hack, and Michael E. Papka, New Frontiers in Leadership Computing Part II[Guest Editorial], Computing in Science & Engineering, 2014
BibTeX
```
@article{HaPa14b,
  author = {Hack, James J. and Papka, Michael E.},
  title = {New Frontiers in Leadership Computing Part II[Guest Editorial]},
  journal = {Computing in Science & Engineering},
  year = {2014},
  volume = {16},
  number = {06},
  pages = {10--12},
  publisher = {IEEE},
}
```
James J. Hack, and Michael E. Papka, New Frontiers in Leadership Computing Part II[Guest Editorial], Computing in Science & Engineering, 2014
BibTeX
```
@article{HaPa14a,
  author = {Hack, James J. and Papka, Michael E.},
  title = {New Frontiers in Leadership Computing Part II[Guest Editorial]},
  journal = {Computing in Science & Engineering},
  year = {2014},
  volume = {16},
  number = {05},
  pages = {10--12},
  publisher = {IEEE},
}
```
Huy Bui, Jason Leigh, Eun-Sung Jungy, Venkatram Vishwanathy, and Michael E. Papka, Improving data movement performance for sparse data patterns on the blue gene/q supercomputer, In 2014 43rd International Conference on Parallel Processing Workshops, 2014
BibTeX
```
@inproceedings{BuLeJu14,
  author = {Bui, Huy and Leigh, Jason and Jungy, Eun-Sung and Vishwanathy, Venkatram and Papka, Michael E.},
  title = {Improving data movement performance for sparse data patterns on the blue gene/q supercomputer},
  booktitle = {2014 43rd International Conference on Parallel Processing Workshops},
  year = {2014},
  pages = {302--311},
  organization = {IEEE},
}
```

Huy Bui, Hal Finkel, Venkatram Vishwanath, Salma Habib, Katrin Heitmann, Jason Leigh, Michael Papka, and Kevin Harms, Scalable parallel I/O on a Blue Gene/Q supercomputer using compression, topology-aware data aggregation, and subfiling, In 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2014

BibTeX

@inproceedings{BuFiVi14,
  author = {Bui, Huy and Finkel, Hal and Vishwanath, Venkatram and Habib, Salma and Heitmann, Katrin and Leigh, Jason and Papka, Michael and Harms, Kevin},
  title = {Scalable parallel I/O on a Blue Gene/Q supercomputer using compression, topology-aware data aggregation, and subfiling},
  booktitle = {2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing},
  year = {2014},
  pages = {107--111},
  organization = {IEEE},
}

Eduardo Berrocal, Li Yu, Sean Wallace, Michael E. Papka, and Zhiling Lan, Exploring void search for fault detection on extreme scale systems, In 2014 IEEE International Conference on Cluster Computing (CLUSTER), 2014
BibTeX
```
@inproceedings{BeYuWa14,
  author = {Berrocal, Eduardo and Yu, Li and Wallace, Sean and Papka, Michael E. and Lan, Zhiling},
  title = {Exploring void search for fault detection on extreme scale systems},
  booktitle = {2014 IEEE International Conference on Cluster Computing (CLUSTER)},
  year = {2014},
  pages = {1--9},
  organization = {IEEE},
}
```

2013

Xu Yang, Zhou Zhou, Sean Wallace, Zhiling Lan, Wei Tang, Susan Coghlan, and Michael E. Papka, Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems, In SC’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2013

BibTeX

@inproceedings{YaZhWa13,
  author = {Yang, Xu and Zhou, Zhou and Wallace, Sean and Lan, Zhiling and Tang, Wei and Coghlan, Susan and Papka, Michael E.},
  title = {Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems},
  booktitle = {SC'13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
  year = {2013},
  pages = {1--11},
  organization = {IEEE},
}

Sean Wallace, Venkatram Vishwanath, Susan Coghlan, Zhiling Lan, and Michael E. Papka, Measuring power consumption on IBM Blue Gene/Q, In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2013
BibTeX
```
@inproceedings{WaViCo13,
  author = {Wallace, Sean and Vishwanath, Venkatram and Coghlan, Susan and Lan, Zhiling and Papka, Michael E.},
  title = {Measuring power consumption on IBM Blue Gene/Q},
  booktitle = {2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum},
  year = {2013},
  pages = {853--859},
  organization = {IEEE},
}
```
Eric R. Schendel, Steve Harenberg, Houjun Tang, Venkatram Vishwanath, Michael E. Papka, and Nagiza F. Samatova, A generic high-performance method for deinterleaving scientific data, In European Conference on Parallel Processing, 2013
BibTeX
```
@inproceedings{ScHaTa13,
  author = {Schendel, Eric R. and Harenberg, Steve and Tang, Houjun and Vishwanath, Venkatram and Papka, Michael E. and Samatova, Nagiza F.},
  title = {A generic high-performance method for deinterleaving scientific data},
  booktitle = {European Conference on Parallel Processing},
  year = {2013},
  pages = {571--582},
  organization = {Springer, Berlin, Heidelberg},
}
```
Khairi Reda, Aaron Knoll, Ken-ichi Nomura, Michael E. Papka, Andrew E. Johnson, and Jason Leigh, Visualizing large-scale atomistic simulations in ultra-resolution immersive environments., In LDAV, 2013
BibTeX
```
@inproceedings{ReKnNo13,
  author = {Reda, Khairi and Knoll, Aaron and Nomura, Ken-ichi and Papka, Michael E. and Johnson, Andrew E. and Leigh, Jason},
  title = {Visualizing large-scale atomistic simulations in ultra-resolution immersive environments.},
  booktitle = {LDAV},
  year = {2013},
  pages = {59--65},
}
```

Khairi Reda, Alessandro Febretti, Aaron Knoll, Jillian Aurisano, Jason Leigh, Andrew Johnson, Michael E. Papka, and Mark Hereld, Visualizing large, heterogeneous data in hybrid-reality environments, IEEE Computer Graphics and Applications, 2013

BibTeX

@article{ReFeKn13,
  author = {Reda, Khairi and Febretti, Alessandro and Knoll, Aaron and Aurisano, Jillian and Leigh, Jason and Johnson, Andrew and Papka, Michael E. and Hereld, Mark},
  title = {Visualizing large, heterogeneous data in hybrid-reality environments},
  journal = {IEEE Computer Graphics and Applications},
  year = {2013},
  volume = {33},
  number = {4},
  pages = {38--48},
  publisher = {IEEE},
}

Michael Papka, Susan Coghlan, Eric Isaacs, Mark Peters, and Paul Messina, Mira: Argonne’s 10-petaflops supercomputer, 2013
BibTeX
```
@techreport{PaCoIs13,
  author = {Papka, Michael and Coghlan, Susan and Isaacs, Eric and Peters, Mark and Messina, Paul},
  title = {Mira: Argonne's 10-petaflops supercomputer},
  year = {2013},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```
Vitali Morozov, Jiayuan Meng, Venkatram Vishwanath, Kalyan Kumaran, and Michael E. Papka, Characterization and Understanding Machine-Specific Interconnects, In International Conference on Parallel Computing Technologies, 2013
BibTeX
```
@inproceedings{MoMeVi13,
  author = {Morozov, Vitali and Meng, Jiayuan and Vishwanath, Venkatram and Kumaran, Kalyan and Papka, Michael E.},
  title = {Characterization and Understanding Machine-Specific Interconnects},
  booktitle = {International Conference on Parallel Computing Technologies},
  year = {2013},
  pages = {90--104},
  organization = {Springer, Berlin, Heidelberg},
}
```
Vitali Morozov, Kalyan Kumaran, Venkatram Vishwanath, Jiayuan Meng, and Michael E. Papka, Early experience on the Blue Gene/Q supercomputing system, In 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013
BibTeX
```
@inproceedings{MoKuVi13,
  author = {Morozov, Vitali and Kumaran, Kalyan and Vishwanath, Venkatram and Meng, Jiayuan and Papka, Michael E.},
  title = {Early experience on the Blue Gene/Q supercomputing system},
  booktitle = {2013 IEEE 27th International Symposium on Parallel and Distributed Processing},
  year = {2013},
  pages = {1229--1240},
  organization = {IEEE},
}
```
Nick Leaf, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, and Kwan-Liu Ma, Efficient parallel volume rendering of large-scale adaptive mesh refinement data, In 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2013
BibTeX
```
@inproceedings{LeViIn13,
  author = {Leaf, Nick and Vishwanath, Venkatram and Insley, Joseph and Hereld, Mark and Papka, Michael E. and Ma, Kwan-Liu},
  title = {Efficient parallel volume rendering of large-scale adaptive mesh refinement data},
  booktitle = {2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)},
  year = {2013},
  pages = {35--42},
  organization = {IEEE},
}
```

Sriram Lakshminarasimhan, David A. Boyuka, Saurabh V. Pendse, Xiaocheng Zou, John Jenkins, Venkatram Vishwanath, Michael E. Papka, and Nagiza F. Samatova, Scalable in situ scientific data encoding for analytical query processing, In Proceedings of the 22nd international symposium on High-performance parallel and distributed computing, 2013

BibTeX

@inproceedings{LaBoPe13,
  author = {Lakshminarasimhan, Sriram and Boyuka, David A. and Pendse, Saurabh V. and Zou, Xiaocheng and Jenkins, John and Vishwanath, Venkatram and Papka, Michael E. and Samatova, Nagiza F.},
  title = {Scalable in situ scientific data encoding for analytical query processing},
  booktitle = {Proceedings of the 22nd international symposium on High-performance parallel and distributed computing},
  year = {2013},
  pages = {1--12},
}

Sidharth Kumar, Avishek Saha, Venkatram Vishwanath, Philip Carns, John A. Schmidt, Giorgio Scorzelli, Hemanth Kolla, Ray Grout, Robert Latham, Robert Ross, Michael E. Papka, Jacqueline Chen, and Valerio Pascucci, Characterization and modeling of PIDX parallel I/O for performance optimization, In SC ’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2013

BibTeX

@inproceedings{KuSaVi13,
  author = {Kumar, Sidharth and Saha, Avishek and Vishwanath, Venkatram and Carns, Philip and Schmidt, John A. and Scorzelli, Giorgio and Kolla, Hemanth and Grout, Ray and Latham, Robert and Ross, Robert and Papka, Michael E. and Chen, Jacqueline and Pascucci, Valerio},
  title = {Characterization and modeling of PIDX parallel I/O for performance optimization},
  booktitle = {SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
  year = {2013},
  pages = {1--12},
  doi = {10.1145/2503210.2503252},
}

Aaron Knoll, Ingo Wald, Paul A. Navrátil, Michael E. Papka, and Kelly P. Gaither, Ray tracing and volume rendering large molecular data on multi-core and many-core architectures, In Proceedings of the 8th International Workshop on Ultrascale Visualization, 2013
BibTeX
```
@inproceedings{KnWaNa13,
  author = {Knoll, Aaron and Wald, Ingo and Navr{\'a}til, Paul A. and Papka, Michael E. and Gaither, Kelly P.},
  title = {Ray tracing and volume rendering large molecular data on multi-core and many-core architectures},
  booktitle = {Proceedings of the 8th International Workshop on Ultrascale Visualization},
  year = {2013},
  pages = {1--8},
}
```
Aaron Knoll, Maria KY Chan, Kah Chun Lau, Bin Liu, Jeffrey Greeley, Larry Curtiss, Mark Hereld, and Michael E. Papka, Uncertainty Classification And Visualization Of Molecular Interfaces, International Journal for Uncertainty Quantification, 2013
BibTeX
```
@article{KnChLa13,
  author = {Knoll, Aaron and Chan, Maria KY and Lau, Kah Chun and Liu, Bin and Greeley, Jeffrey and Curtiss, Larry and Hereld, Mark and Papka, Michael E.},
  title = {Uncertainty Classification And Visualization Of Molecular Interfaces},
  journal = {International Journal for Uncertainty Quantification},
  year = {2013},
  volume = {3},
  number = {2},
}
```

Nicholas T. Karonis, Kirk L. Duffin, Caesar E. Ordoñez, Bela Erdelyi, Thomas D. Uram, Eric C. Olson, George Coutrakon, and Michael E. Papka, Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT), Journal of Parallel and Distributed Computing, 2013

BibTeX

@article{KaDuOr13,
  author = {Karonis, Nicholas T. and Duffin, Kirk L. and Ordo{\~n}ez, Caesar E. and Erdelyi, Bela and Uram, Thomas D. and Olson, Eric C. and Coutrakon, George and Papka, Michael E.},
  title = {Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)},
  journal = {Journal of Parallel and Distributed Computing},
  year = {2013},
  volume = {73},
  number = {12},
  pages = {1605--1612},
  publisher = {Academic Press},
}

Leopold Grinberg, Vitali Morozov, Dmitry A. Fedosov, Joseph Insley, Michael Papka, Kalyan Kumaran, and George Karniadakis, Multiscale simulation of blood flow in brain arteries with an aneurysm, 2013
BibTeX
```
@techreport{GrMoFe13,
  author = {Grinberg, Leopold and Morozov, Vitali and Fedosov, Dmitry A. and Insley, Joseph and Papka, Michael and Kumaran, Kalyan and Karniadakis, George},
  title = {Multiscale simulation of blood flow in brain arteries with an aneurysm},
  year = {2013},
  institution = {Argonne National Lab.(ANL), Argonne, IL (United States)},
}
```
Richard M. Coffey, Beth A. Cerny, James R. Collins, Ahn Chel Heinzel, John J. Spizzirri, Laura K. Wolf, Paul C. Messina, Michael E. Papka, and Katherine M. Riley, Argonne Leadership Computing Facility 2013 Science Highlights, 2013
BibTeX
```
@techreport{CoCeCo13,
  author = {Coffey, Richard M. and Cerny, Beth A. and Collins, James R. and Heinzel, Ahn Chel and Spizzirri, John J. and Wolf, Laura K. and Messina, Paul C. and Papka, Michael E. and Riley, Katherine M.},
  title = {Argonne Leadership Computing Facility 2013 Science Highlights},
  year = {2013},
  institution = {Argonne National Laboratory (ANL), Argonne, IL (United States)},
}
```

Anna Maria Bailey, Adam Bertsch, Barna Bihari, Brian Carnes, Kimberly Cupps, Erik W. Draeger, Larry Fried, Mark Gary, James N. Glosli, John C. Gyllenhaal, Steven Langer, Rose McCallen, Arthur A. Mirin, Fady Najjar, Albert Nichols, Terri Quinn, David Richards, Tom Spelce, Becky Springmeyer, Fred Streitz, Bronis Supinski, Pavlos Vranas, Dong Chen, George L.T. Chiu, Paul W. Coteus, Thomas W. Fox, Thomas Gooding, John A. Gunnels, Ruud A. Haring, Philip Heidelberger, Todd Inglett, Kyu-hyoun Kim, Amith R. Mamidala, Sam Miller, Mike Nelson, Martin Ohmacht, Fabrizio Petrini, Kyung Dong Ryu, Andrew A. Schram, Robert Shearer, Robert E. Walkup, Amy Wang, Robert W. Wisniewski, William E. Allcock, Charles Bacon, Raymond Bair, Ramesh Balakrishnan, Richard Coffey, Susan Coghlan, Jeff Hammond, Mark Hereld, Kalyan Kumaran, Paul Messina, Vitali Morozov, Michael E. Papka, Katherine M. Riley, Nichols A. Romero, and Timothy J. Williams, Blue Gene/Q: Sequoia and Mira, In Contemporary High Performance Computing, 2013

BibTeX

@incollection{BaBeBi13,
  author = {Bailey, Anna Maria and Bertsch, Adam and Bihari, Barna and Carnes, Brian and Cupps, Kimberly and Draeger, Erik W. and Fried, Larry and Gary, Mark and Glosli, James N. and Gyllenhaal, John C. and Langer, Steven and McCallen, Rose and Mirin, Arthur A. and Najjar, Fady and Nichols, Albert and Quinn, Terri and Richards, David and Spelce, Tom and Springmeyer, Becky and Streitz, Fred and de Supinski, Bronis and Vranas, Pavlos and Chen, Dong and Chiu, George L.T. and Coteus, Paul W. and Fox, Thomas W. and Gooding, Thomas and Gunnels, John A. and Haring, Ruud A. and Heidelberger, Philip and Inglett, Todd and Kim, Kyu-hyoun and Mamidala, Amith R. and Miller, Sam and Nelson, Mike and Ohmacht, Martin and Petrini, Fabrizio and Ryu, Kyung Dong and Schram, Andrew A. and Shearer, Robert and Walkup, Robert E. and Wang, Amy and Wisniewski, Robert W. and Allcock, William E. and Bacon, Charles and Bair, Raymond and Balakrishnan, Ramesh and Coffey, Richard and Coghlan, Susan and Hammond, Jeff and Hereld, Mark and Kumaran, Kalyan and Messina, Paul and Morozov, Vitali and Papka, Michael E. and Riley, Katherine M. and Romero, Nichols A. and Williams, Timothy J.},
  title = {Blue Gene/Q: Sequoia and Mira},
  booktitle = {Contemporary High Performance Computing},
  year = {2013},
  pages = {225--281},
  publisher = {Chapman and Hall/CRC},
}

2012

David P. Schissel, Geni Abla, Justin R. Burruss, Elliot Feibush, Thomas W. Fredian, Monte M. Goode, Martin J. Greenwald, Katarzyna Keahey, Torrance Leggett, Kai Li, Doug C. McCune, Michael E. Papka, Lew Randerson, Allen Sanderson, Joshua Stillerman, Mary R. Thompson, Thomas Uram, and Grant Wallace, A National Collaboratory to Advance the Science of High Temperature Plasma Physics for Magnetic Fusion, 2012

BibTeX

@techreport{ScAbBu12,
  author = {Schissel, David P. and Abla, Geni and Burruss, Justin R. and Feibush, Elliot and Fredian, Thomas W. and Goode, Monte M. and Greenwald, Martin J. and Keahey, Katarzyna and Leggett, Torrance and Li, Kai and McCune, Doug C. and Papka, Michael E. and Randerson, Lew and Sanderson, Allen and Stillerman, Joshua and Thompson, Mary R. and Uram, Thomas and Wallace, Grant},
  title = {A National Collaboratory to Advance the Science of High Temperature Plasma Physics for Magnetic Fusion},
  year = {2012},
  number = {GA–A25540 Rev. 1},
  institution = {General Atomics, CA (United States)},
}

M. E. Papka, P Messina, R Coffey, and C Drugan, Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing., 2012
BibTeX
```
@techreport{PaMeCo12,
  author = {Papka, M. E. and Messina, P and Coffey, R and Drugan, C},
  title = {Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing.},
  year = {2012},
  institution = {Argonne National Laboratory (ANL)},
}
```
Vitali Morozov, Jiayuan Meng, Venkatram Vishwanath, Jeff R. Hammond, Kalyan Kumaran, and Michael E. Papka, ALCF MPI benchmarks: Understanding machine-specific communication behavior, In 2012 41st International Conference on Parallel Processing Workshops, 2012
BibTeX
```
@inproceedings{MoMeVi12,
  author = {Morozov, Vitali and Meng, Jiayuan and Vishwanath, Venkatram and Hammond, Jeff R. and Kumaran, Kalyan and Papka, Michael E.},
  title = {ALCF MPI benchmarks: Understanding machine-specific communication behavior},
  booktitle = {2012 41st International Conference on Parallel Processing Workshops},
  year = {2012},
  pages = {19--28},
  organization = {IEEE},
}
```
Shusen Liu, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, and Valerio Pascucci, A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters, In Large-Scale Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on, 2012
BibTeX
```
@inproceedings{LiViIn12,
  author = {Liu, Shusen and Vishwanath, Venkatram and Insley, Joseph and Hereld, Mark and Papka, Michael E. and Pascucci, Valerio},
  title = {A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters},
  booktitle = {Large-Scale Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on},
  year = {2012},
}
```

Sidharth Kumar, Venkatram Vishwanath, Philip Carns, Joshua A. Levine, Robert Latham, Giorgio Scorzelli, Hemanth Kolla, Ray Grout, Robert Ross, Michael E. Papka, Jacqueline Chen, and Valerio Pascucci, Efficient data restructuring and aggregation for I/O acceleration in PIDX, In SC ’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2012

BibTeX

@inproceedings{KuViCa12,
  author = {Kumar, Sidharth and Vishwanath, Venkatram and Carns, Philip and Levine, Joshua A. and Latham, Robert and Scorzelli, Giorgio and Kolla, Hemanth and Grout, Ray and Ross, Robert and Papka, Michael E. and Chen, Jacqueline and Pascucci, Valerio},
  title = {Efficient data restructuring and aggregation for I/O acceleration in PIDX},
  booktitle = {SC '12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
  year = {2012},
  pages = {1--11},
  doi = {10.1109/sc.2012.54},
}

Natallia Kotava, Aaron Knoll, Mathias Schott, Christoph Garth, Xavier Tricoche, Christoph Kessler, Elaine Cohen, Charles D. Hansen, Michael E. Papka, and Hans Hagen, Volume rendering with multidimensional peak finding, In 2012 IEEE Pacific Visualization Symposium, 2012

BibTeX

@inproceedings{KoKnSc12,
  author = {Kotava, Natallia and Knoll, Aaron and Schott, Mathias and Garth, Christoph and Tricoche, Xavier and Kessler, Christoph and Cohen, Elaine and Hansen, Charles D. and Papka, Michael E. and Hagen, Hans},
  title = {Volume rendering with multidimensional peak finding},
  booktitle = {2012 IEEE Pacific Visualization Symposium},
  year = {2012},
  pages = {161--168},
  organization = {IEEE},
}

Aaron Knoll, Joe Insley, Michael E. Papka, Ken-ichi Nomura, Rajiv K. Kalia, Aiichiro Nakano, and Priya Vashishta, Molecular Dynamics Simulation of Amorphous SiO2 Fracture, In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012
BibTeX
```
@inproceedings{KnInPa12,
  author = {Knoll, Aaron and Insley, Joe and Papka, Michael E. and Nomura, Ken-ichi and Kalia, Rajiv K. and Nakano, Aiichiro and Vashishta, Priya},
  title = {Molecular Dynamics Simulation of Amorphous SiO2 Fracture},
  booktitle = {2012 SC Companion: High Performance Computing, Networking Storage and Analysis},
  year = {2012},
  pages = {1569--1571},
  organization = {IEEE},
}
```

Joseph A. Insley, Mark Hereld, Michael E. Papka, Rick Wagner, Robert Harkness, Michael L. Norman, and Daniel R. Reynolds, Direct Numerical Simulations of Cosmological Reionization: Field Comparison: Ionization Fraction, In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012

BibTeX

@inproceedings{InHePa12,
  author = {Insley, Joseph A. and Hereld, Mark and Papka, Michael E. and Wagner, Rick and Harkness, Robert and Norman, Michael L. and Reynolds, Daniel R.},
  title = {Direct Numerical Simulations of Cosmological Reionization: Field Comparison: Ionization Fraction},
  booktitle = {2012 SC Companion: High Performance Computing, Networking Storage and Analysis},
  year = {2012},
  pages = {1574--1575},
  organization = {IEEE},
}

Leopold Grinberg, Joseph A. Insley, Dmitry A. Fedosov, Vitali A. Morozov, Michael E. Papka, and George Em Karniadakis, Tightly Coupled Atomistic-Continuum Simulations of Brain Blood Flow on Petaflop Supercomputers, IEEE Computing in Science & Engineering, 2012
BibTeX
```
@article{GrInFe12,
  author = {Grinberg, Leopold and Insley, Joseph A. and Fedosov, Dmitry A. and Morozov, Vitali A. and Papka, Michael E. and Karniadakis, George Em},
  title = {Tightly Coupled Atomistic-Continuum Simulations of Brain Blood Flow on Petaflop Supercomputers},
  journal = {IEEE Computing in Science & Engineering},
  year = {2012},
  volume = {14},
  number = {6},
  pages = {58--67},
}
```

Kirk L. Duffin, Nicholas T. Karonis, Caesar E. Ordoñez, Michael E. Papka, George Coutrakon, Bela Erdelyi, Eric C. Olson, and Thomas D. Uram, An analysis of a distributed gpu implementation of proton computed tomographic (pct) reconstruction, In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012

BibTeX

@inproceedings{DuKaOr12,
  author = {Duffin, Kirk L. and Karonis, Nicholas T. and Ordo{\~n}ez, Caesar E. and Papka, Michael E. and Coutrakon, George and Erdelyi, Bela and Olson, Eric C. and Uram, Thomas D.},
  title = {An analysis of a distributed gpu implementation of proton computed tomographic (pct) reconstruction},
  booktitle = {2012 SC Companion: High Performance Computing, Networking Storage and Analysis},
  year = {2012},
  pages = {166--175},
  organization = {IEEE},
}

George Coutrakon, Kirk Duffin, Bela Erdelyi, Nicholas Karonis, Eric Olson, Caesar Ordoñez, Michael E. Papka, and Thomas Uram, High-Performance Multiple-CPU/GPU Proton Computed Tomography, Transactions, 2012

BibTeX

@article{CoDuEr12,
  author = {Coutrakon, George and Duffin, Kirk and Erdelyi, Bela and Karonis, Nicholas and Olson, Eric and Ordo{\~n}ez, Caesar and Papka, Michael E. and Uram, Thomas},
  title = {High-Performance Multiple-CPU/GPU Proton Computed Tomography},
  journal = {Transactions},
  year = {2012},
  volume = {106},
  number = {1},
  pages = {70--72},
  publisher = {American Nuclear Society},
}

Dennis Chau, Bradley McGinnis, Jonas Talandis, Jason Leigh, Tom Peterka, Aaron Knoll, Aslihan Sumer, Michael Papka, and Julius Jellinek, A simultaneous 2D/3D autostereo workstation, In Stereoscopic Displays and Applications XXIII, 2012

BibTeX

@inproceedings{ChMcTa12,
  author = {Chau, Dennis and McGinnis, Bradley and Talandis, Jonas and Leigh, Jason and Peterka, Tom and Knoll, Aaron and Sumer, Aslihan and Papka, Michael and Jellinek, Julius},
  title = {A simultaneous 2D/3D autostereo workstation},
  booktitle = {Stereoscopic Displays and Applications XXIII},
  year = {2012},
  volume = {8288},
  pages = {923--932},
  organization = {Spie},
}

Huy Bui, Venkatram Vishwanath, Jason Leigh, and Michael E. Papka, Evaluating Communication Performance in BlueGene/Q and Cray XE6 Supercomputers., In SC Companion, 2012
BibTeX
```
@inproceedings{BuViLe12b,
  author = {Bui, Huy and Vishwanath, Venkatram and Leigh, Jason and Papka, Michael E.},
  title = {Evaluating Communication Performance in BlueGene/Q and Cray XE6 Supercomputers.},
  booktitle = {SC Companion},
  year = {2012},
  pages = {1514},
}
```
Huy Bui, Venkatram Vishwanath, Jason Leigh, and Michael E. Papka, Poster: Evaluating Communication Performance in BlueGene/Q and Cray XE6 Supercomputers, In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012
BibTeX
```
@inproceedings{BuViLe12a,
  author = {Bui, Huy and Vishwanath, Venkatram and Leigh, Jason and Papka, Michael E.},
  title = {Poster: Evaluating Communication Performance in BlueGene/Q and Cray XE6 Supercomputers},
  booktitle = {2012 SC Companion: High Performance Computing, Networking Storage and Analysis},
  year = {2012},
  pages = {1515--1515},
  organization = {IEEE},
}
```

2011

Venkatram Vishwanath, Mark Hereld, Michael E. Papka, Randy Hudson, G Cal Jordan IV, and Christopher Daley, In situ data analysis and I/O acceleration of FLASH astrophysics simulation on leadership-class system using GLEAN, In SciDAC 2011 Conference, 2011
BibTeX
```
@inproceedings{ViHePa11b,
  author = {Vishwanath, Venkatram and Hereld, Mark and Papka, Michael E. and Hudson, Randy and Jordan IV, G Cal and Daley, Christopher},
  title = {In situ data analysis and I/O acceleration of FLASH astrophysics simulation on leadership-class system using GLEAN},
  booktitle = {SciDAC 2011 Conference},
  year = {2011},
}
```
Venkatram Vishwanath, Mark Hereld, and Michael E. Papka, Toward simulation-time data analysis and i/o acceleration on leadership-class systems, In 2011 IEEE Symposium on Large Data Analysis and Visualization, 2011
BibTeX
```
@inproceedings{ViHePa11a,
  author = {Vishwanath, Venkatram and Hereld, Mark and Papka, Michael E.},
  title = {Toward simulation-time data analysis and i/o acceleration on leadership-class systems},
  booktitle = {2011 IEEE Symposium on Large Data Analysis and Visualization},
  year = {2011},
  pages = {9--14},
  organization = {IEEE},
}
```
Venkatram Vishwanath, Mark Hereld, Vitali Morozov, and Michael E. Papka, Topology-aware data movement and staging for I/O acceleration on Blue Gene/P supercomputing systems, In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, 2011
BibTeX
```
@inproceedings{ViHeMo11,
  author = {Vishwanath, Venkatram and Hereld, Mark and Morozov, Vitali and Papka, Michael E.},
  title = {Topology-aware data movement and staging for I/O acceleration on Blue Gene/P supercomputing systems},
  booktitle = {Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2011},
  pages = {1--11},
  organization = {IEEE},
}
```
Thomas D. Uram, Michael E. Papka, Mark Hereld, and Michael Wilde, A solution looking for lots of problems: Generic portals for science infrastructure, In Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, 2011
BibTeX
```
@inproceedings{UrPaHe11,
  author = {Uram, Thomas D. and Papka, Michael E. and Hereld, Mark and Wilde, Michael},
  title = {A solution looking for lots of problems: Generic portals for science infrastructure},
  booktitle = {Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery},
  year = {2011},
  pages = {1--7},
}
```

Michel Rasquin, Patrick Marion, Venkatram Vishwanath, Benjamin Matthews, Mark Hereld, Kenneth Jansen, Raymond Loy, Andrew Bauer, Min Zhou, Onkar Sahni, Jing Fu, Ning Liu, Christopher Carothers, Mark Shephard, Michael E. Papka, Kalyan Kumaran, and Berk Geveci, Electronic Poster: Co-Visualization of Full Data and in Situ Data Extracts from Unstructured Grid CFD at 160K Cores, In Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion, 2011

Abstract BibTeX

Scalability and time-to-solution studies have historically been focused on the size of the problem and run time. We consider a more strict definition of "solution" whereby a live data analysis (co-visualization of either the full data or in situ data extracts) provides continuous and reconfigurable insight into massively parallel simulations. Specifically, we used the Argonne Leadership Class Facility’s (ALCF) BlueGene/P machine with 163,840 cores tightly linked through a high-speed network to 100 visualization nodes that share 800 cores and 200 GPUs. Three meshes with respectively 52M, 416M and 3.3B elements discretize the flow over a full swept wing with an unsteady synthetic jet to evaluate time-to-solution plus insight. On the full machine, the 416M element mesh takes about 2 seconds per flow solve step including the extraction and rendering of a slice or a contour, slowing currently the simulation by only 10 and 15% respectively. The 3.3B element case proved scalable at about 15 seconds per time step, whereas PHASTA’s strong scaling could compress the time-to-solution for the 52M element case enough to allow the rendering of one frame (slice or contour) every 0.7 second, paving the way for interactive simulation and simulation steering on massively parallel systems1.

@inproceedings{RaMaVi11,
  author = {Rasquin, Michel and Marion, Patrick and Vishwanath, Venkatram and Matthews, Benjamin and Hereld, Mark and Jansen, Kenneth and Loy, Raymond and Bauer, Andrew and Zhou, Min and Sahni, Onkar and Fu, Jing and Liu, Ning and Carothers, Christopher and Shephard, Mark and Papka, Michael E. and Kumaran, Kalyan and Geveci, Berk},
  title = {{Electronic Poster: Co-Visualization of Full Data and in Situ Data Extracts from Unstructured Grid CFD at 160K Cores}},
  booktitle = {Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion},
  year = {2011},
  pages = {103–104},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {SC '11 Companion},
  doi = {10.1145/2148600.2148653},
  isbn = {9781450310307},
  keywords = {live data visualization, in situ and full data extract, paraview coprocessing library, phasta cfd solver, glean i/o forwarding tool, data reduction},
  location = {Seattle, Washington, USA},
  numpages = {2},
  url = {https://doi.org/10.1145/2148600.2148653},
}

Kenneth Moreland, Ron Oldfield, Pat Marion, Sebastien Jourdain, Norbert Podhorszki, Venkatram Vishwanath, Nathan Fabian, Ciprian Docan, Manish Parashar, Mark Hereld, Michael E. Papka, and Scott Klasky, Examples of in Transit Visualization, In Proceedings of the 2nd International Workshop on Petascal Data Analytics: Challenges and Opportunities, 2011

Abstract BibTeX

One of the most pressing issues with petascale analysis is the transport of simulation results data to a meaningful analysis. Traditional workflow prescribes storing the simulation results to disk and later retrieving them for analysis and visualization. However, at petascale this storage of the full results is prohibitive. A solution to this problem is to run the analysis and visualization concurrently with the simulation and bypass the storage of the full results. One mechanism for doing so is in transit visualization in which analysis and visualization is run on I/O nodes that receive the full simulation results but write information from analysis or provide run-time visualization. This paper describes the work in progress for three in transit visualization solutions, each using a different transport mechanism.

@inproceedings{MoOlMa11,
  author = {Moreland, Kenneth and Oldfield, Ron and Marion, Pat and Jourdain, Sebastien and Podhorszki, Norbert and Vishwanath, Venkatram and Fabian, Nathan and Docan, Ciprian and Parashar, Manish and Hereld, Mark and Papka, Michael E. and Klasky, Scott},
  title = {Examples of in Transit Visualization},
  booktitle = {Proceedings of the 2nd International Workshop on Petascal Data Analytics: Challenges and Opportunities},
  year = {2011},
  pages = {1–6},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Pdac '11},
  doi = {10.1145/2110205.2110207},
  isbn = {9781450311304},
  keywords = {staging, in transit, in situ, parallel scientific visualization},
  location = {Seattle, Washington, USA},
  numpages = {6},
  url = {https://doi.org/10.1145/2110205.2110207},
}

Aaron Knoll, Sebastian Thelen, Ingo Wald, Charles D. Hansen, Hans Hagen, and Michael E. Papka, Full-resolution interactive CPU volume rendering with coherent BVH traversal, In 2011 IEEE Pacific Visualization Symposium, 2011
BibTeX
```
@inproceedings{KnThWa11,
  author = {Knoll, Aaron and Thelen, Sebastian and Wald, Ingo and Hansen, Charles D. and Hagen, Hans and Papka, Michael E.},
  title = {Full-resolution interactive CPU volume rendering with coherent BVH traversal},
  booktitle = {2011 IEEE Pacific Visualization Symposium},
  year = {2011},
  pages = {3--10},
  organization = {IEEE},
}
```
Aaron Knoll, Tom Peterka, Mark Hereld, Michael E. Papka, Bin Liu, Maria Chan, and Jeffrey Greeley, Vis and Analysis for Computational Energy Materials Research, In SciDAC 2011 Conference, 2011
BibTeX
```
@inproceedings{KnPeHe11,
  author = {Knoll, Aaron and Peterka, Tom and Hereld, Mark and Papka, Michael E. and Liu, Bin and Chan, Maria and Greeley, Jeffrey},
  title = {Vis and Analysis for Computational Energy Materials Research},
  booktitle = {SciDAC 2011 Conference},
  year = {2011},
}
```

Joseph A. Insley, Rick Wagner, Robert Harkness, Daniel R. Reynolds, Michael L. Norman, Mark Hereld, Eric C. Olson, Michael E. Papka, and Venkatram Vishwanath, Electronic Poster: Modeling Early Galaxies Using Radiation Hydrodynamics, In Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion, 2011

Abstract BibTeX

This simulation uses a flux-limited diffusion solver to explore the radiation hydrodynamics of early galaxies, in particular, the ionizing radiation created by Population III stars. At the time of this rendering, the simulation has evolved to a redshift of 3.5. The simulation volume is 11.2 comoving megaparsecs, and has a uniform grid of 10243 cells, with over 1 billion dark matter and star particles.This animation shows a combined view of the baryon density, dark matter density, radiation energy and emissivity from this simulation. The multi-variate rendering is particularly useful because is shows both the baryonic matter ("normal") and dark matter, and the pressure and temperature variables are properties of only the baryonic matter. Visible in the gas density are "bubbles", or shells, created by the radiation feedback from young stars. Seeing the bubbles from feedback provides confirmation of the physics model implemented. Features such as these are difficult to identify algorithmically, but easily found when viewing the visualization

@inproceedings{InWaHa11,
  author = {Insley, Joseph A. and Wagner, Rick and Harkness, Robert and Reynolds, Daniel R. and Norman, Michael L. and Hereld, Mark and Olson, Eric C. and Papka, Michael E. and Vishwanath, Venkatram},
  title = {{Electronic Poster: Modeling Early Galaxies Using Radiation Hydrodynamics}},
  booktitle = {Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion},
  year = {2011},
  pages = {141–142},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {SC '11 Companion},
  doi = {10.1145/2148600.2148674},
  isbn = {9781450310307},
  keywords = {visualization, modeling, astrophysics},
  location = {Seattle, Washington, USA},
  numpages = {2},
  url = {https://doi.org/10.1145/2148600.2148674},
}

Joseph A. Insley, Leopold Grinberg, Michael E. Papka, and George Karniadakis, Electronic Poster: Visualizing Multiscale Simulation Data, In Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion, 2011

Abstract BibTeX

Accurately modeling many physical and biological systems requires simulating at multiple scales. This results in large and heterogeneous data sets on vastly differing scales, both physical and temporal. Here we look specifically at blood flow in a patient-specific cerebrovasculature with a brain aneurysm, and analyze the interaction of platelets with the arterial walls that lead to thrombus formation.

@inproceedings{InGrPa11c,
  author = {Insley, Joseph A. and Grinberg, Leopold and Papka, Michael E. and Karniadakis, George},
  title = {{Electronic Poster: Visualizing Multiscale Simulation Data}},
  booktitle = {Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion},
  year = {2011},
  pages = {101–102},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {SC '11 Companion},
  doi = {10.1145/2148600.2148652},
  isbn = {9781450310307},
  keywords = {visualization, modeling, multi-scale},
  location = {Seattle, Washington, USA},
  numpages = {2},
  url = {https://doi.org/10.1145/2148600.2148652},
}

Joseph A. Insley, Leopold Grinberg, and Michael E. Papka, Visualization of multiscale simulation data: brain blood flow, In Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, 2011
BibTeX
```
@inproceedings{InGrPa11b,
  author = {Insley, Joseph A. and Grinberg, Leopold and Papka, Michael E.},
  title = {Visualization of multiscale simulation data: brain blood flow},
  booktitle = {Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery},
  year = {2011},
  pages = {1--2},
}
```
Joseph A. Insley, Leopold Grinberg, and Michael E. Papka, Visualizing multiscale, multiphysics simulation data: Brain blood flow, In 2011 IEEE Symposium on Large Data Analysis and Visualization, 2011
BibTeX
```
@inproceedings{InGrPa11a,
  author = {Insley, Joseph A. and Grinberg, Leopold and Papka, Michael E.},
  title = {Visualizing multiscale, multiphysics simulation data: Brain blood flow},
  booktitle = {2011 IEEE Symposium on Large Data Analysis and Visualization},
  year = {2011},
  pages = {3--7},
  organization = {IEEE},
}
```
Joseph A. Insley, Leopold Grinberg, Dmitry A. Fedosov, Vitali Morozov, Bruce Caswell, Michael E. Papka, and Geroge Em Karniadakis, Blood Flow: Multi-Scale Modeling and Visualization, In Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion, 2011
Abstract BibTeX

Multi-scale modeling of arterial blood flow can shed light on the interaction between events happening at micro- and meso-scales (i.e., adhesion of red blood cells to the arterial wall, clot formation) and at macro-scales (i.e., change in flow patterns due to the clot). Coupled numerical simulations of such multi-scale flow require state-of-the-art computers and algorithms, along with techniques for multi-scale visualizations.This animation presents results of two studies used in the development of a multi-scale visualization methodology. The first illustrates a flow of healthy (red) and diseased (blue) blood cells with a Dissipative Particle Dynamics (DPD) method. Each blood cell is represented by a mesh made of 500 DPD-particles, and small spheres show a sub-set of the DPD particles representing the blood plasma, while instantaneous streamlines and slices represent the ensemble average velocity. In the second we investigate the process of thrombus (blood clot) formation, which may be responsible for the rupture of aneurysms, by concentrating on the platelet blood cells, observing as they aggregate on the wall of an aneurysm. The ability to use a single integrated tool for the visualization of this multi-scale simulation data is important to understanding the effects of the large-scale flow patterns on the detailed particle behavior.
```
@inproceedings{InGrFe11,
  author = {Insley, Joseph A. and Grinberg, Leopold and Fedosov, Dmitry A. and Morozov, Vitali and Caswell, Bruce and Papka, Michael E. and Karniadakis, Geroge Em},
  title = {Blood Flow: Multi-Scale Modeling and Visualization},
  booktitle = {Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion},
  year = {2011},
  pages = {139–140},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {SC '11 Companion},
  doi = {10.1145/2148600.2148673},
  isbn = {9781450310307},
  keywords = {multi-scale, visualization, modeling},
  location = {Seattle, Washington, USA},
  numpages = {2},
  url = {https://doi.org/10.1145/2148600.2148673},
}
```
Randy Hudson, John Norris, Lynn B. Reid, G Cal Jordan IV, Klaus Weide, and Michael E. Papka, Data-intensive management and analysis for scientific simulations, In Proceedings of the Ninth Australasian Symposium on Parallel and Distributed Computing-Volume 118, 2011
BibTeX
```
@inproceedings{HuNoRe11b,
  author = {Hudson, Randy and Norris, John and Reid, Lynn B. and Jordan IV, G Cal and Weide, Klaus and Papka, Michael E.},
  title = {Data-intensive management and analysis for scientific simulations},
  booktitle = {Proceedings of the Ninth Australasian Symposium on Parallel and Distributed Computing-Volume 118},
  year = {2011},
  pages = {13--14},
}
```
Randy Hudson, John Norris, Lynn B. Reid, Klaus Weide, G Cal Jordan IV, and Michael E. Papka, Experiences using smaash to manage data-intensive simulations, In Proceedings of the 20th international symposium on High performance distributed computing, 2011
BibTeX
```
@inproceedings{HuNoRe11a,
  author = {Hudson, Randy and Norris, John and Reid, Lynn B. and Weide, Klaus and Jordan IV, G Cal and Papka, Michael E.},
  title = {Experiences using smaash to manage data-intensive simulations},
  booktitle = {Proceedings of the 20th international symposium on High performance distributed computing},
  year = {2011},
  pages = {205--216},
}
```
Mark Hereld, Michael E. Papka, Joseph A. Insley, Michael L. Norman, Eric C. Olson, and Rick Wagner, Interactive large data exploration over the wide area, In Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, 2011
BibTeX
```
@inproceedings{HePaIn11,
  author = {Hereld, Mark and Papka, Michael E. and Insley, Joseph A. and Norman, Michael L. and Olson, Eric C. and Wagner, Rick},
  title = {Interactive large data exploration over the wide area},
  booktitle = {Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery},
  year = {2011},
  pages = {1--2},
}
```
Mark Hereld, Joseph A. Insley, Eric C. Olson, Michael E. Papka, Venkatram Vishwanath, Michael L. Norman, and Rick Wagner, Exploring large data over wide area networks, In 2011 IEEE Symposium on Large Data Analysis and Visualization, 2011
BibTeX
```
@inproceedings{HeInOl11,
  author = {Hereld, Mark and Insley, Joseph A. and Olson, Eric C. and Papka, Michael E. and Vishwanath, Venkatram and Norman, Michael L. and Wagner, Rick},
  title = {Exploring large data over wide area networks},
  booktitle = {2011 IEEE Symposium on Large Data Analysis and Visualization},
  year = {2011},
  pages = {133--134},
  organization = {IEEE},
}
```

Leopold Grinberg, Joseph A. Insley, Vitali Morozov, Michael E. Papka, George Em Karniadakis, Dmitry Fedosov, and Kalyan Kumaran, A new computational paradigm in multiscale simulations: Application to brain blood flow, In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, 2011

BibTeX

@inproceedings{GrInMo11,
  author = {Grinberg, Leopold and Insley, Joseph A. and Morozov, Vitali and Papka, Michael E. and Karniadakis, George Em and Fedosov, Dmitry and Kumaran, Kalyan},
  title = {A new computational paradigm in multiscale simulations: Application to brain blood flow},
  booktitle = {Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2011},
  pages = {1--5},
}

Jack Dongarra, Pete Beckman, Terry Moore, Patrick Aerts, Giovanni Aloisio, Jean-Claude Andre, David Barkai, Jean-Yves Berthou, Taisuke Boku, Bertrand Braunschweig, Franck Cappello, Barbara Chapman, Xuebin Chi, Alok Choudhary, Sudip Dosanjh, Thom Dunning, Sandro Fiore, Al Geist, Bill Gropp, Robert Harrison, Mark Hereld, Michael Heroux, Adolfy Hoisie, Koh Hotta, Zhong Jin, Yutaka Ishikawa, Fred Johnson, Sanjay Kale, Richard Kenway, David Keyes, Bill Kramer, Jesus Labarta, Alain Lichnewsky, Thomas Lippert, Bob Lucas, Barney Maccabe, Satoshi Matsuoka, Paul Messina, Peter Michielse, Bernd Mohr, Matthias S. Mueller, Wolfgang E. Nagel, Hiroshi Nakashima, Michael E. Papka, Dan Reed, Mitsuhisa Sato, Ed Seidel, John Shalf, David Skinner, Marc Snir, Thomas Sterling, Rick Stevens, Fred Streitz, Bob Sugar, Shinji Sumimoto, William Tang, John Taylor, Rajeev Thakur, Anne Trefethen, Mateo Valero, Aad Steen, Jeffrey Vetter, Peg Williams, Robert Wisniewski, and Kathy Yelick, The International Exascale Software Project roadmap, The International Journal of High Performance Computing Applications, 2011

Abstract BibTeX

Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

@article{DoBeMo11,
  author = {Dongarra, Jack and Beckman, Pete and Moore, Terry and Aerts, Patrick and Aloisio, Giovanni and Andre, Jean-Claude and Barkai, David and Berthou, Jean-Yves and Boku, Taisuke and Braunschweig, Bertrand and Cappello, Franck and Chapman, Barbara and Chi, Xuebin and Choudhary, Alok and Dosanjh, Sudip and Dunning, Thom and Fiore, Sandro and Geist, Al and Gropp, Bill and Harrison, Robert and Hereld, Mark and Heroux, Michael and Hoisie, Adolfy and Hotta, Koh and Jin, Zhong and Ishikawa, Yutaka and Johnson, Fred and Kale, Sanjay and Kenway, Richard and Keyes, David and Kramer, Bill and Labarta, Jesus and Lichnewsky, Alain and Lippert, Thomas and Lucas, Bob and Maccabe, Barney and Matsuoka, Satoshi and Messina, Paul and Michielse, Peter and Mohr, Bernd and Mueller, Matthias S. and Nagel, Wolfgang E. and Nakashima, Hiroshi and Papka, Michael E. and Reed, Dan and Sato, Mitsuhisa and Seidel, Ed and Shalf, John and Skinner, David and Snir, Marc and Sterling, Thomas and Stevens, Rick and Streitz, Fred and Sugar, Bob and Sumimoto, Shinji and Tang, William and Taylor, John and Thakur, Rajeev and Trefethen, Anne and Valero, Mateo and van der Steen, Aad and Vetter, Jeffrey and Williams, Peg and Wisniewski, Robert and Yelick, Kathy},
  title = {The International Exascale Software Project roadmap},
  journal = {The International Journal of High Performance Computing Applications},
  year = {2011},
  volume = {25},
  number = {1},
  pages = {3--60},
  doi = {10.1177/1094342010391989},
  eprint = {https://doi.org/10.1177/1094342010391989},
  url = {https://doi.org/10.1177/1094342010391989},
}

Sean Ahern, Arie Shoshani, Kwan-Liu Ma, Alok Choudhary, Terence Critchlow, Scott Klasky, Valerio Pascucci, Jim Ahrens, E Wes Bethel, Hank Childs, Jian Huang, Ken Joy, Quincey Koziol, Gerald Lofstead, Jeremy Meredith, Kenneth Moreland, George Ostrouchov, Michael E. Papka, Venkatram Vishwanath, Matthew Wolf, Nicholas Wright, and Kesheng Wu, Scientific Discovery at the Exascale: Report from the DOE ASCR 2011 Workshop on Exascale Data Management, Department of Energy Office of Advanced Scientific Computing Research, 2011

BibTeX

@article{AhShMa11,
  author = {Ahern, Sean and Shoshani, Arie and Ma, Kwan-Liu and Choudhary, Alok and Critchlow, Terence and Klasky, Scott and Pascucci, Valerio and Ahrens, Jim and Bethel, E Wes and Childs, Hank and Huang, Jian and Joy, Ken and Koziol, Quincey and Lofstead, Gerald and Meredith, Jeremy and Moreland, Kenneth and Ostrouchov, George and Papka, Michael E. and Vishwanath, Venkatram and Wolf, Matthew and Wright, Nicholas and Wu, Kesheng},
  title = {{Scientific Discovery at the Exascale: Report from the DOE ASCR 2011 Workshop on Exascale Data Management}},
  journal = {Department of Energy Office of Advanced Scientific Computing Research},
  year = {2011},
  volume = {1},
  address = {Houston, TX},
}

2010

Wenjun Wu, Thomas Uram, Michael Wilde, Mark Hereld, and Michael E. Papka, Accelerating science gateway development with Web 2.0 and Swift, In Proceedings of the 2010 TeraGrid Conference, 2010
BibTeX
```
@inproceedings{WuUrWi10b,
  author = {Wu, Wenjun and Uram, Thomas and Wilde, Michael and Hereld, Mark and Papka, Michael E.},
  title = {Accelerating science gateway development with Web 2.0 and Swift},
  booktitle = {Proceedings of the 2010 TeraGrid Conference},
  year = {2010},
  pages = {1--7},
}
```
Wenjun Wu, Thomas D. Uram, Michael Wilde, Mark Hereld, and Michael E. Papka, A Web 2.0-Based Scientific Application Framework, In 2010 IEEE International Conference on Web Services, 2010
BibTeX
```
@inproceedings{WuUrWi10a,
  author = {Wu, Wenjun and Uram, Thomas D. and Wilde, Michael and Hereld, Mark and Papka, Michael E.},
  title = {A Web 2.0-Based Scientific Application Framework},
  booktitle = {2010 IEEE International Conference on Web Services},
  year = {2010},
  pages = {642--643},
  organization = {IEEE},
}
```

Venkatram Vishwanath, Mark Hereld, Kamil Iskra, Dries Kimpe, Vitali Morozov, Michael E. Papka, Robert Ross, and Kazutomo Yoshii, Accelerating i/o forwarding in ibm blue gene/p systems, In SC’10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, 2010

BibTeX

@inproceedings{ViHeIs10,
  author = {Vishwanath, Venkatram and Hereld, Mark and Iskra, Kamil and Kimpe, Dries and Morozov, Vitali and Papka, Michael E. and Ross, Robert and Yoshii, Kazutomo},
  title = {Accelerating i/o forwarding in ibm blue gene/p systems},
  booktitle = {SC'10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis},
  year = {2010},
  pages = {1--10},
  organization = {IEEE},
}

Sidharth Kumar, Valerio Pascucci, Venkatram Vishwanath, Philip Carns, Mark Hereld, Robert Latham, Tom Peterka, Michael E. Papka, and Robert Ross, Towards parallel access of multi-dimensional, multi-resolution scientific data, In 2010 5th Petascale Data Storage Workshop (PDSW’10), 2010

BibTeX

@inproceedings{KuPaVi10,
  author = {Kumar, Sidharth and Pascucci, Valerio and Vishwanath, Venkatram and Carns, Philip and Hereld, Mark and Latham, Robert and Peterka, Tom and Papka, Michael E. and Ross, Robert},
  title = {Towards parallel access of multi-dimensional, multi-resolution scientific data},
  booktitle = {2010 5th Petascale Data Storage Workshop (PDSW'10)},
  year = {2010},
  pages = {1--5},
  organization = {IEEE},
}

2009

Wenjun Wu, Thomas Uram, and Michael E. Papka, Web 2.0-based social informatics data grid, In Proceedings of the 5th Grid Computing Environments Workshop, 2009
BibTeX
```
@inproceedings{WuUrPa09,
  author = {Wu, Wenjun and Uram, Thomas and Papka, Michael E.},
  title = {Web 2.0-based social informatics data grid},
  booktitle = {Proceedings of the 5th Grid Computing Environments Workshop},
  year = {2009},
  pages = {1--7},
}
```
Michael E. Papka, Visualization and Collaboration Technologies to Support High Performance Computing Research, 2009
BibTeX
```
@misc{Pa09,
  author = {Papka, Michael E.},
  title = {Visualization and Collaboration Technologies to Support High Performance Computing Research},
  year = {2009},
  publisher = {University of Chicago},
}
```
Joseph A. Insley, Torrance Leggett, and Michael E. Papka, Using dynamic accounts to enable access to advanced resources through science gateways, In Proceedings of the 5th Grid Computing Environments Workshop, 2009
BibTeX
```
@inproceedings{InLePa09,
  author = {Insley, Joseph A. and Leggett, Torrance and Papka, Michael E.},
  title = {Using dynamic accounts to enable access to advanced resources through science gateways},
  booktitle = {Proceedings of the 5th Grid Computing Environments Workshop},
  year = {2009},
  pages = {1--6},
}
```
Mark Hereld, Joseph A. Insley, Eric C. Olson, Michael E. Papka, Thomas D. Uram, and Venkatram Vishwanath, Modeling resource-coupled computations, In Proceedings of the 2009 Workshop on Ultrascale Visualization, 2009
BibTeX
```
@inproceedings{HeInOl09,
  author = {Hereld, Mark and Insley, Joseph A. and Olson, Eric C. and Papka, Michael E. and Uram, Thomas D. and Vishwanath, Venkatram},
  title = {Modeling resource-coupled computations},
  booktitle = {Proceedings of the 2009 Workshop on Ultrascale Visualization},
  year = {2009},
  pages = {27--33},
}
```
Mark Hereld, Randy Hudson, John Norris, Michael E. Papka, and Thomas Uram, Enabling scientific teamwork, In Journal of Physics: Conference Series, 2009
BibTeX
```
@inproceedings{HeHuNo09,
  author = {Hereld, Mark and Hudson, Randy and Norris, John and Papka, Michael E. and Uram, Thomas},
  title = {Enabling scientific teamwork},
  booktitle = {Journal of Physics: Conference Series},
  year = {2009},
  volume = {180},
  number = {1},
  pages = {012085},
  organization = {IOP Publishing},
}
```

2008

Wenjun Wu, Michael E. Papka, and Rick Stevens, Toward an opensocial life science gateway, In 2008 Grid Computing Environments Workshop, 2008
BibTeX
```
@inproceedings{WuPaSt08,
  author = {Wu, Wenjun and Papka, Michael E. and Stevens, Rick},
  title = {Toward an opensocial life science gateway},
  booktitle = {2008 Grid Computing Environments Workshop},
  year = {2008},
  pages = {1--6},
  organization = {IEEE},
}
```
Wenjun Wu, Rob Edwards, Ivan R. Judson, Michael E. Papka, Mary Thomas, and Rick Stevens, TeraGrid Open Life Science Gateway, In TeraGrid 2008 Conference, 2008
BibTeX
```
@inproceedings{WuEdJu08,
  author = {Wu, Wenjun and Edwards, Rob and Judson, Ivan R. and Papka, Michael E. and Thomas, Mary and Stevens, Rick},
  title = {TeraGrid Open Life Science Gateway},
  booktitle = {TeraGrid 2008 Conference},
  year = {2008},
  pages = {9--13},
}
```
Mark Hereld, Michael E. Papka, and Thomas D. Uram, Collaboration as a second thought, In 2008 International Symposium on Collaborative Technologies and Systems, 2008
BibTeX
```
@inproceedings{HePaUr08,
  author = {Hereld, Mark and Papka, Michael E. and Uram, Thomas D.},
  title = {Collaboration as a second thought},
  booktitle = {2008 International Symposium on Collaborative Technologies and Systems},
  year = {2008},
  pages = {196--202},
  organization = {IEEE},
}
```
Mark Hereld, and Michael E. Papka, The data analysis computing hierarchy, In 2008 Workshop on Ultrascale Visualization, 2008
BibTeX
```
@inproceedings{HePa08,
  author = {Hereld, Mark and Papka, Michael E.},
  title = {The data analysis computing hierarchy},
  booktitle = {2008 Workshop on Ultrascale Visualization},
  year = {2008},
  pages = {9--12},
  organization = {IEEE},
}
```
M Hereld, E Olson, ME Papka, and TD Uram, Streaming visualization for collaborative environments, In Journal of Physics: Conference Series, 2008
BibTeX
```
@inproceedings{HeOlPa08,
  author = {Hereld, M and Olson, E and Papka, ME and Uram, TD},
  title = {Streaming visualization for collaborative environments},
  booktitle = {Journal of Physics: Conference Series},
  year = {2008},
  volume = {125},
  number = {1},
  pages = {012097},
  organization = {IOP Publishing},
}
```

Robert T. Fisher, Leo P. Kadanoff, Donald Q. Lamb, Anshu Dubey, Tomasz Plewa, Alan Calder, Fausto Cattaneo, Peter Constantin, Ian Foster, Michael E. Papka, Snezhana I. Abarzhi, S. M. Asida, Paul M. Rich, Chad C. Glendenin, Katie Antypas, Daniel J. Sheeler, Lynn B. Reid, Brad Gallagher, and Shawn G. Needham, Terascale turbulence computation using the FLASH3 application framework on the IBM Blue Gene/L system, IBM Journal of Research and Development, 2008

BibTeX

@article{FiKaLa08,
  author = {Fisher, Robert T. and Kadanoff, Leo P. and Lamb, Donald Q. and Dubey, Anshu and Plewa, Tomasz and Calder, Alan and Cattaneo, Fausto and Constantin, Peter and Foster, Ian and Papka, Michael E. and Abarzhi, Snezhana I. and Asida, S. M. and Rich, Paul M. and Glendenin, Chad C. and Antypas, Katie and Sheeler, Daniel J. and Reid, Lynn B. and Gallagher, Brad and Needham, Shawn G.},
  title = {Terascale turbulence computation using the {FLASH3} application framework on the {IBM Blue Gene/L} system},
  journal = {IBM Journal of Research and Development},
  year = {2008},
  volume = {52},
  number = {1.2},
  pages = {127--136},
  doi = {10.1147/rd.521.0127},
}

Charlie Catlett, William E. Allcock, Phil Andrews, Ruth Aydt, Ray Bair, Natasha Balac, Bryan Banister, Trish Barker, Mark Bartelt, Pete Beckman, Francine Berman, Gary Bertoline, Alan Blatecky, Jay Boisseau, Jim Bottum, Sharon Brunett, Julian Bunn, Michelle Butler, David Carver, John Cobb, Tim Cockerill, Peter F. Couvares, Maytal Dahan, Diana Diehl, Thom Dunning, Ian Foster, Kelly Gaither, Dennis Gannon, Sebastien Goasguen, Michael Grobe, Dave Hart, Matt Heinzel, Chris Hempel, Wendy Huntoon, Joseph Insley, Christopher Jordan, Ivan Judson, Anke Kamrath, Nicholas Karonis, Carl Kesselman, Patricia Kovatch, Lex Lane, Scott Lathrop, Michael Levine, David Lifka, Lee Liming, Miron Livny, Rich Loft, Doru Marcusiu, Jim Marsteller, Stuart Martin, Scott McCaulay, John McGee, Laura McGinnis, Michael McRobbie, Paul Messina, Reagan Moore, Richard Moore, J.P. Navarro, Jeff Nichols, Michael E. Papka, Rob Pennington, Greg Pike, Jim Pool, Raghu Reddy, Dan Reed, Tony Rimovsky, Eric Roberts, Ralph Roskies, Sergiu Sanielevici, J. Ray Scott, Anurag Shankar, Mark Sheddon, Mike Showerman, Derek Simmel, Abe Singer, Dane Skow, Shava Smallen, Warren Smith, Carol Song, Rick Stevens, Craig Stewart, Robert B. Stock, Nathan Stone, John Towns, Tomislav Urban, Mike Vildibill, Edward Walker, Von Welch, Nancy Wilkins-Diehr, Roy Williams, Linda Winkler, Lan Zhao, and Ann Zimmerman, High Performance Computing and Grids in Action, 2008

BibTeX

@inbook{CaAlAn08,
  author = {Catlett, Charlie and Allcock, William E. and Andrews, Phil and Aydt, Ruth and Bair, Ray and Balac, Natasha and Banister, Bryan and Barker, Trish and Bartelt, Mark and Beckman, Pete and Berman, Francine and Bertoline, Gary and Blatecky, Alan and Boisseau, Jay and Bottum, Jim and Brunett, Sharon and Bunn, Julian and Butler, Michelle and Carver, David and Cobb, John and Cockerill, Tim and Couvares, Peter F. and Dahan, Maytal and Diehl, Diana and Dunning, Thom and Foster, Ian and Gaither, Kelly and Gannon, Dennis and Goasguen, Sebastien and Grobe, Michael and Hart, Dave and Heinzel, Matt and Hempel, Chris and Huntoon, Wendy and Insley, Joseph and Jordan, Christopher and Judson, Ivan and Kamrath, Anke and Karonis, Nicholas and Kesselman, Carl and Kovatch, Patricia and Lane, Lex and Lathrop, Scott and Levine, Michael and Lifka, David and Liming, Lee and Livny, Miron and Loft, Rich and Marcusiu, Doru and Marsteller, Jim and Martin, Stuart and McCaulay, Scott and McGee, John and McGinnis, Laura and McRobbie, Michael and Messina, Paul and Moore, Reagan and Moore, Richard and Navarro, J.P. and Nichols, Jeff and Papka, Michael E. and Pennington, Rob and Pike, Greg and Pool, Jim and Reddy, Raghu and Reed, Dan and Rimovsky, Tony and Roberts, Eric and Roskies, Ralph and Sanielevici, Sergiu and Scott, J. Ray and Shankar, Anurag and Sheddon, Mark and Showerman, Mike and Simmel, Derek and Singer, Abe and Skow, Dane and Smallen, Shava and Smith, Warren and Song, Carol and Stevens, Rick and Stewart, Craig and Stock, Robert B. and Stone, Nathan and Towns, John and Urban, Tomislav and Vildibill, Mike and Walker, Edward and Welch, Von and Wilkins-Diehr, Nancy and Williams, Roy and Winkler, Linda and Zhao, Lan and Zimmerman, Ann},
  editor = {Grandinetti, Lucio},
  title = {High Performance Computing and Grids in Action},
  year = {2008},
  volume = {16},
  pages = {225--249},
  publisher = {IOS Press},
  chapter = {TeraGrid: Analysis of Organization, System Architecture, and Middleware Enabling New Types of Applications},
}

2007

Jonathan C. Silverstein, Colin Walsh, Fred Dech, Eric C. Olson, Michael E. Papka, Nigel M. Parsad, and Rick L. Stevens, Immersive virtual anatomy course using a cluster of volume visualization machines and passive stereo., In Mmvr, 2007
BibTeX
```
@inproceedings{SiWaDe07,
  author = {Silverstein, Jonathan C. and Walsh, Colin and Dech, Fred and Olson, Eric C. and Papka, Michael E. and Parsad, Nigel M. and Stevens, Rick L.},
  title = {Immersive virtual anatomy course using a cluster of volume visualization machines and passive stereo.},
  booktitle = {Mmvr},
  year = {2007},
  pages = {439--444},
}
```
Gina-Anne Levow, Bennett Bertenthal, Mark Hereld, Sarah Kenny, David McNeill, Michael Papka, and Sonjia Waxmonsky, SIDGRID: A Framework for Distributed and Integrated Multimodal Annotation and Archiving and and Analysis, In Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue, 2007
BibTeX
```
@inproceedings{LeBeHe07,
  author = {Levow, Gina-Anne and Bertenthal, Bennett and Hereld, Mark and Kenny, Sarah and McNeill, David and Papka, Michael and Waxmonsky, Sonjia},
  title = {SIDGRID: A Framework for Distributed and Integrated Multimodal Annotation and Archiving and and Analysis},
  booktitle = {Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue},
  year = {2007},
  pages = {231--234},
}
```
Chris Johnson, Robert Ross, S Ahern, J Ahrens, W Bethel, KL Ma, M Papka, JV Rosendale, HW Shen, and J Thomas, Visualization and knowledge discovery: Report from the DOE/ASCR workshop on visual analysis and data exploration at extreme scale, Salt Lake City, 2007
BibTeX
```
@article{JoRoAh07,
  author = {Johnson, Chris and Ross, Robert and Ahern, S and Ahrens, J and Bethel, W and Ma, KL and Papka, M and Rosendale, JV and Shen, HW and Thomas, J},
  title = {Visualization and knowledge discovery: Report from the DOE/ASCR workshop on visual analysis and data exploration at extreme scale},
  journal = {Salt Lake City},
  year = {2007},
}
```
Joseph A. Insley, Michael E. Papka, Suchuan Dong, George Karniadakis, and Nicholas T. Karonis, Runtime visualization of the human arterial tree, IEEE Transactions on Visualization and Computer Graphics, 2007
BibTeX
```
@article{InPaDo07,
  author = {Insley, Joseph A. and Papka, Michael E. and Dong, Suchuan and Karniadakis, George and Karonis, Nicholas T.},
  title = {Runtime visualization of the human arterial tree},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2007},
  volume = {13},
  number = {4},
  pages = {810--821},
  publisher = {IEEE},
}
```

Robert L. Grossman, Michal Sabala, Shirley Connelly, Yunhong Gu, Matt Handley, Rajmonda Sulo, Dave Turkington, Anushka Anand, Leland Wilkinson, Ian Foster, Ti Leggett, Michael E. Papka, Mike Wilde, Joe Mambretti, Bob Lucas, and John Tran, Angle: Detecting Anomalies and Emergent Behavior from Distributed Data in Near Real Time [Analytics Challenge Winner], In SC07 International Conference for High Performance Computing, Networking, Storage and Analysis, Reno, NV, 2007

BibTeX

@inproceedings{GrSaCo07,
  author = {Grossman, Robert L. and Sabala, Michal and Connelly, Shirley and Gu, Yunhong and Handley, Matt and Sulo, Rajmonda and Turkington, Dave and Anand, Anushka and Wilkinson, Leland and Foster, Ian and Leggett, Ti and Papka, Michael E. and Wilde, Mike and Mambretti, Joe and Lucas, Bob and Tran, John},
  title = {Angle: Detecting Anomalies and Emergent Behavior from Distributed Data in Near Real Time [Analytics Challenge Winner]},
  booktitle = {SC07 International Conference for High Performance Computing, Networking, Storage and Analysis, Reno, NV},
  year = {2007},
}

Justin Binns, Jonathan DiCarlo, Joseph A. Insley, Torrance Leggett, Cory Lueninghoener, John-Paul Navarro, and Michael E. Papka, Enabling community access to TeraGrid visualization resources, Concurrency and Computation: Practice and Experience, 2007

BibTeX

@article{BiDiIn07,
  author = {Binns, Justin and DiCarlo, Jonathan and Insley, Joseph A. and Leggett, Torrance and Lueninghoener, Cory and Navarro, John-Paul and Papka, Michael E.},
  title = {Enabling community access to TeraGrid visualization resources},
  journal = {Concurrency and Computation: Practice and Experience},
  year = {2007},
  volume = {19},
  number = {6},
  pages = {783--794},
  publisher = {Wiley Online Library},
}

Bennett Bertenthal, Robert Grossman, David Hanley, Mark Hereld, Sarah Kenny, Gina Levow, Michael E. Papka, Steve Porges, Kavithaa Rajavenkateshwaran, Rick Stevens, Thomas D. Uram, and Wenjun Wu, Social informatics data grid, In Third International Conference on e-Social Science, 2007
BibTeX
```
@inproceedings{BeGrHa07,
  author = {Bertenthal, Bennett and Grossman, Robert and Hanley, David and Hereld, Mark and Kenny, Sarah and Levow, Gina and Papka, Michael E. and Porges, Steve and Rajavenkateshwaran, Kavithaa and Stevens, Rick and Uram, Thomas D. and Wu, Wenjun},
  title = {Social informatics data grid},
  booktitle = {Third International Conference on e-Social Science},
  year = {2007},
  pages = {7--9},
}
```
Svenja Adolphs, Bennett Bertenthal, Steve Boker, Ronald Carter, Chris Greenhalgh, Mark Hereld, Sarah Kenny, Gina Levow, Michael E. Papka, and Tony Pridmore, Integrating cyberinfrastructure into existing e-Social Science research, In Proceedings of the e-Social Science 2007 Conference, 2007
BibTeX
```
@inproceedings{AdBeBo07,
  author = {Adolphs, Svenja and Bertenthal, Bennett and Boker, Steve and Carter, Ronald and Greenhalgh, Chris and Hereld, Mark and Kenny, Sarah and Levow, Gina and Papka, Michael E. and Pridmore, Tony},
  title = {Integrating cyberinfrastructure into existing e-Social Science research},
  booktitle = {Proceedings of the e-Social Science 2007 Conference},
  year = {2007},
}
```

2006

Mark Hereld, Michael E. Papka, Justin Binns, and Rick L. Stevens, CupHolder: A Multi-Person Interactive High-Resolution Workstation, In IEEE Virtual Reality Conference (VR 2006), 2006
BibTeX
```
@inproceedings{HePaBi06,
  author = {Hereld, Mark and Papka, Michael E. and Binns, Justin and Stevens, Rick L.},
  title = {CupHolder: A Multi-Person Interactive High-Resolution Workstation},
  booktitle = {IEEE Virtual Reality Conference (VR 2006)},
  year = {2006},
  pages = {323--323},
  organization = {IEEE},
}
```
Han Gao, Michael E. Papka, and Rick L. Stevens, Extending Multicast Communications by Hybrid Overlay Network, In 2006 IEEE International Conference on Communications, 2006
BibTeX
```
@inproceedings{GaPaSt06,
  author = {Gao, Han and Papka, Michael E. and Stevens, Rick L.},
  title = {Extending Multicast Communications by Hybrid Overlay Network},
  booktitle = {2006 IEEE International Conference on Communications},
  year = {2006},
  volume = {2},
  pages = {820--828},
  organization = {IEEE},
}
```
Suchuan Dong, Joseph Insley, Nicholas T. Karonis, Michael E. Papka, Justin Binns, and George Karniadakis, Simulating and visualizing the human arterial system on the TeraGrid, Future Generation Computer Systems, 2006
BibTeX
```
@article{DoInKa06,
  author = {Dong, Suchuan and Insley, Joseph and Karonis, Nicholas T. and Papka, Michael E. and Binns, Justin and Karniadakis, George},
  title = {Simulating and visualizing the human arterial system on the TeraGrid},
  journal = {Future Generation Computer Systems},
  year = {2006},
  volume = {22},
  number = {8},
  pages = {1011--1017},
  publisher = {North-Holland},
}
```

Katie B. Antypas, Alan C. Calder, Anshu Dubey, J. Bradley Gallagher, J. Joshi, Donald Q. Lamb, Timur Linde, Ewing L. Lusk, O.E. Bronson Messer, Andrea Mignone, H. Pan, Michael E. Papka, F. Peng, Tomas Plewa, Katherine M. Riley, Paul M. Ricker, Daniel Sheeler, Andrew Siegel, N. Taylor, James W. Truran, Natalia Vladimirova, Greg Weirs, D. Yu, and J. Zhang, FLASH: Applications and Future, In Parallel Computational Fluid Dynamics 2005, 2006

Abstract BibTeX

Flash has been successful in simulating a wide variety of astrophysical problems, both within the flash center and in the external community. The code has steadily gained acceptance since its initial release for the following reasons: (1) it is easily ported to a variety of computer architectures and the distribution includes support for many standard machines; (2) creation of new applications is straightforward and well-documented, and the suite of tools allows users to quickly process and interpret simulation results; (3) and the modularity of the code allows users to readily add functionality. The unusually large scale of flash necessitates the adoption of practices already in wide commercial use but not typically found in an academic scientific project. The principal lesson learned during the development of flash is the importance of good software practices. These include thorough documentation, detailed design specifications for the framework and components, and especially regression testing, which is essential in a project with multiple developers.

@incollection{AnCaDu06,
  author = {Antypas, Katie B. and Calder, Alan C. and Dubey, Anshu and Gallagher, J. Bradley and Joshi, J. and Lamb, Donald Q. and Linde, Timur and Lusk, Ewing L. and Messer, O.E. Bronson and Mignone, Andrea and Pan, H. and Papka, Michael E. and Peng, F. and Plewa, Tomas and Riley, Katherine M. and Ricker, Paul M. and Sheeler, Daniel and Siegel, Andrew and Taylor, N. and Truran, James W. and Vladimirova, Natalia and Weirs, Greg and Yu, D. and Zhang, J.},
  editor = {Deane, Anil and Ecer, Akin and McDonough, James and Satofuka, Nobuyuki and Brenner, Gunther and Emerson, David R. and Periaux, Jacques and Tromeur-Dervout, Damien},
  title = {FLASH: Applications and Future},
  booktitle = {Parallel Computational Fluid Dynamics 2005},
  year = {2006},
  pages = {325--331},
  publisher = {Elsevier},
  address = {Amsterdam},
  doi = {https://doi.org/10.1016/B978-044452206-1/50039-2},
  isbn = {978-0-444-52206-1},
  url = {https://www.sciencedirect.com/science/article/pii/B9780444522061500392},
}

James Ahrens, Hank Childs, John Clyne, Wes Bethel, Jian Huang, Scott Klasky, Kwan-Liu Ma, Kenneth Moreland, Michael Papka, Valerio Pascucci, Han-Wei Shen, and Debroah Silver, Workshop on Ultra-Scale Visualization, In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, 2006

Abstract BibTeX

The output from the massively parallel scientific simulations is so voluminous and complex that advanced visualization technologies are necessary to interpret the calculated results. Even though visualization technology has progressed significantly in recent years, we are barely capable of visualizing and analyzing terascale data to its full extent, and petascale datasets are on the horizon. This workshop aims at addressing this pressing issue by fostering communication between visualization researchers and practitioners. The workshop attendees will be introduced to the latest and greatest research innovations in large data visualization and also help direct further research direction through an open discussion session.

@inproceedings{AhChCl06,
  author = {Ahrens, James and Childs, Hank and Clyne, John and Bethel, Wes and Huang, Jian and Klasky, Scott and Ma, Kwan-Liu and Moreland, Kenneth and Papka, Michael and Pascucci, Valerio and Shen, Han-Wei and Silver, Debroah},
  title = {Workshop on Ultra-Scale Visualization},
  booktitle = {Proceedings of the 2006 ACM/IEEE Conference on Supercomputing},
  year = {2006},
  pages = {235–es},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Sc '06},
  doi = {10.1145/1188455.1188700},
  isbn = {0769527000},
  location = {Tampa, Florida},
  url = {https://doi.org/10.1145/1188455.1188700},
}

2005

Jonathan C. Silverstein, Fred Dech, Justin Binns, David Jones, Michael E. Papka, and Rick Stevens, Distributed collaborative radiological visualization using access grid., Studies in Health Technology and Informatics, 2005
BibTeX
```
@article{SiDeBi05,
  author = {Silverstein, Jonathan C. and Dech, Fred and Binns, Justin and Jones, David and Papka, Michael E. and Stevens, Rick},
  title = {Distributed collaborative radiological visualization using access grid.},
  journal = {Studies in Health Technology and Informatics},
  year = {2005},
  volume = {111},
  pages = {477--481},
}
```
Katarzyna Keahey, Michael E. Papka, Q Peng, David Schissel, Geni Abla, Takuya Araki, Justin Burruss, Elliot Feibush, Peter Lane, Scott Klasky, Torrance Leggett, Doug Mccune, and Lew Randerson, Grid Support for Collaborative Control Room in Fusion Science, Cluster Computing,Oct, 2005
Abstract BibTeX

The National Fusion Collaboratory project seeks to enable fusion scientists to exploit Grid capabilities in support of experimental science. To this end we are exploring the concept of a collaborative control room that harnesses Grid and collaborative technologies to provide an environment in which remote experimental devices, codes, and expertise can interact in real time during an experiment. This concept has the potential to make fusion experiments more efficient by enabling researchers to perform more analysis and by engaging more expertise from a geographically distributed team of scientists and resources. As the realities of software development, talent distribution, and budgets increasingly encourage pooling resources and specialization, we see such environments as a necessary tool for future science.In this paper, we describe an experimental mock-up of a remote interaction with the DIII-D control room. The collaborative control room was demonstrated at SC03 and later reviewed at an international ITER Grid Workshop. We describe how the combined effect of various technologies–collaborative, visualization, and Grid–can be used effectively in experimental science. Specifically, we describe the Access Grid, experimental data presentation tools, and agreement-based resource management and workflow systems enabling time-bounded end-to-end application execution. We also report on FusionGrid services whose use during the fusion experimental cycle became possible for the first time thanks to this technology, and we discuss its potential use in future fusion experiments.
```
@article{KePaPe05,
  author = {Keahey, Katarzyna and Papka, Michael E. and Peng, Q and Schissel, David and Abla, Geni and Araki, Takuya and Burruss, Justin and Feibush, Elliot and Lane, Peter and Klasky, Scott and Leggett, Torrance and Mccune, Doug and Randerson, Lew},
  title = {Grid Support for Collaborative Control Room in Fusion Science},
  journal = {Cluster Computing},
  year = {2005},
  month = oct,
  volume = {8},
  number = {4},
  pages = {305–311},
  publisher = {Kluwer Academic Publishers},
  address = {Usa},
  doi = {10.1007/s10586-005-4097-z},
  issn = {1386-7857},
  numpages = {7},
  url = {https://doi.org/10.1007/s10586-005-4097-z},
}
```
Robert Grossman, Michal Sabala, Anushka Aanand, Steve Eick, Leland Wilkinson, Pei Zhang, John Chaves, Steve Vejcik, John Dillenburg, Peter Nelson, Doug Rorem, Javid Alimohideen, Jason Leigh, Michael E. Papka, and Rick Stevens, Real Time Change Detection and Alerts from Highway Traffic Data, In SC ’05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing,Nov, 2005
Abstract BibTeX

We developed a testbed containing: real time data from over 830 highway traffic sensors in the Chicago region, data about weather, and text data about events that might affect traffic. The goal was to detect in real time interesting changes in traffic conditions. Given the size and complexity of the data, we choose to build a large number of separate baseline models. We built a separate baseline for each hour in the day, for each day in the week, and for every 2 or 3 traffic sensors, resulting in over 42,000 separate baseline models. We also built a baseline engine to build the necessary baselines automatically. We modified an open source scoring engine to process in real time each new sensor reading, update the appropriate feature vectors, score the updated feature vectors using the baseline models, and send out real time alerts when deviations from the baselines were detected.
```
@inproceedings{GrSaAa05,
  author = {Grossman, Robert and Sabala, Michal and Aanand, Anushka and Eick, Steve and Wilkinson, Leland and Zhang, Pei and Chaves, John and Vejcik, Steve and Dillenburg, John and Nelson, Peter and Rorem, Doug and Alimohideen, Javid and Leigh, Jason and Papka, Michael E. and Stevens, Rick},
  title = {Real Time Change Detection and Alerts from Highway Traffic Data},
  booktitle = {SC '05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing},
  year = {2005},
  month = nov,
  pages = {69--69},
  doi = {10.1109/sc.2005.60},
}
```
Han Gao, Michael E. Papka, and Rick L. Stevens, Performance metrics of IP multicast sessions, In Seventh IEEE International Symposium on Multimedia (ISM’05), 2005
BibTeX
```
@inproceedings{GaPaSt05,
  author = {Gao, Han and Papka, Michael E. and Stevens, Rick L.},
  title = {Performance metrics of IP multicast sessions},
  booktitle = {Seventh IEEE International Symposium on Multimedia (ISM'05)},
  year = {2005},
  pages = {10--pp},
  organization = {IEEE},
}
```

Han Gao, Ivan R. Judson, Thomas Uram, Susanne Lefvert, Terry L. Disz, Michael E. Papka, and Rick L. Stevens, An Infrastructure of Network Services for Seamless Integration in Advanced Collaborative Computing Environments, In 2005 IEEE International Conference on Cluster Computing, 2005

BibTeX

@inproceedings{GaJuUr05,
  author = {Gao, Han and Judson, Ivan R. and Uram, Thomas and Lefvert, Susanne and Disz, Terry L. and Papka, Michael E. and Stevens, Rick L.},
  title = {An Infrastructure of Network Services for Seamless Integration in Advanced Collaborative Computing Environments},
  booktitle = {2005 IEEE International Conference on Cluster Computing},
  year = {2005},
  pages = {1--10},
  organization = {IEEE},
}

H Gao, IR Judson, ME Papka, and RL Stevens, Middleware of Performance Analysis for Multimedia Grid, In Proceedings of the 30th IEEE Conference on Local Computer Networks, 2005
BibTeX
```
@inproceedings{GaJuPa05,
  author = {Gao, H and Judson, IR and Papka, ME and Stevens, RL},
  title = {Middleware of Performance Analysis for Multimedia Grid},
  booktitle = {Proceedings of the 30th IEEE Conference on Local Computer Networks},
  year = {2005},
}
```

Justin R. Burruss, Geni Abla, Scott Flanagan, Katarzyna Keahey, Torrance Leggett, Christiane Ludesche, Doug McCune, Michael E. Papka, Qian Peng, Lew Randerson, and David P. Schissel, Developments in Remote Collaboration and Computation, Fusion Science and Technology, 2005

BibTeX

@article{BuAbFl05,
  author = {Burruss, Justin R. and Abla, Geni and Flanagan, Scott and Keahey, Katarzyna and Leggett, Torrance and Ludesche, Christiane and McCune, Doug and Papka, Michael E. and Peng, Qian and Randerson, Lew and Schissel, David P.},
  title = {Developments in Remote Collaboration and Computation},
  journal = {Fusion Science and Technology},
  year = {2005},
  volume = {47},
  number = {3},
  pages = {814--818},
  publisher = {Taylor & Francis},
}

Justin Binns, Fred Dech, Matthew McCrory, Michael E. Papka, Jonathan C. Silverstein, and Rick Stevens, Developing a distributed collaborative radiological visualization application, Studies in Health Technology and Informatics, 2005

BibTeX

@article{BiDeMc05,
  author = {Binns, Justin and Dech, Fred and McCrory, Matthew and Papka, Michael E. and Silverstein, Jonathan C. and Stevens, Rick},
  title = {Developing a distributed collaborative radiological visualization application},
  journal = {Studies in Health Technology and Informatics},
  year = {2005},
  volume = {112},
  pages = {70--79},
  publisher = {Amsterdam; Washington, DC: IOS Press, 1991-},
}

2004

David Schissel, Katarzyna Keahey, Doug McCune, Justin Burruss, Lew Randerson, Scott Klasky, Geni Abla, Peter Lane, Elliot Feibush, Michael E. Papka, Torrance Leggett, Takuya Araki, and Qian Peng, Grids for Experimental Science: The Virtual Control Room, In Proceedings of the Second International Workshop on Challenges of Large Applications in Distributed Environments,Jun, 2004
Abstract BibTeX

The National Fusion Collaboratory focuses on enabling fusion scientists to explore Grid capabilities in support of experimental science. Fusion experiments are structured as a series of plasma pulses initiated roughly every 20 minutes. In the between-pulse intervals scientists perform data analysis and discuss results to reach decisions affecting changes to the next plasma pulse. This interaction can be made more efficient by performing more analysis and engaging more expertise from a geographically distributed team of scientists and resources. In this paper, we describe a virtual control room experiment that unites collaborative, visualization, and Grid technologies to provide such environment and shows how their combined effect can advance experimental science. We also report on FusionGrid services whose use during the fusion experimental cycle became possible for the first time thanks to this technology. We also describe the Access Grid, experimental data presentation tools, and agreement-based resource management and workflow systems enabling time-bounded end-to-end application execution. The first virtual control room experiment represented a mock-up of a remote interaction with the DIII-D control room and was presented at SC03 and later reviewed at an international ITER Grid Workshop.
```
@inproceedings{ScKeMc04,
  author = {Schissel, David and Keahey, Katarzyna and McCune, Doug and Burruss, Justin and Randerson, Lew and Klasky, Scott and Abla, Geni and Lane, Peter and Feibush, Elliot and Papka, Michael E. and Leggett, Torrance and Araki, Takuya and Peng, Qian},
  title = {Grids for Experimental Science: The Virtual Control Room},
  booktitle = {Proceedings of the Second International Workshop on Challenges of Large Applications in Distributed Environments},
  year = {2004},
  month = jun,
  pages = {4},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  doi = {10.1109/clade.2004.1309087},
  keywords = {null},
  url = {https://doi.IEEEcomputersociety.org/10.1109/CLADE.2004.1309087},
}
```

David P. Schissel, Katarzyna Keahey, Takuya Araki, Justin R. Burruss, Elliot Feibush, S M Flanagan, Ian Foster, Thomas W. Fredian, Martin J. Greenwald, Scott A. Klasky, Torrance Leggett, Kai Li, Doug C. McCune, Peter Lane, Michael E. Papka, Q Peng, Lew Randerson, Allen Sanderson, Joshua Stillerman, Mary R. Thompson, and Grant Wallace, The National Fusion Collaboratory Project: Applying Grid Technology for Magnetic Fusion Research, In Workshop on Case Studies on Grid Applications, 2004

BibTeX

@inproceedings{ScKeAr04,
  author = {Schissel, David P. and Keahey, Katarzyna and Araki, Takuya and Burruss, Justin R. and Feibush, Elliot and Flanagan, S M and Foster, Ian and Fredian, Thomas W. and Greenwald, Martin J. and Klasky, Scott A. and Leggett, Torrance and Li, Kai and McCune, Doug C. and Lane, Peter and Papka, Michael E. and Peng, Q and Randerson, Lew and Sanderson, Allen and Stillerman, Joshua and Thompson, Mary R. and Wallace, Grant},
  title = {The National Fusion Collaboratory Project: Applying Grid Technology for Magnetic Fusion Research},
  booktitle = {Workshop on Case Studies on Grid Applications},
  year = {2004},
}

David P. Schissel, J.R Burruss, Adam Finkelstein, S M Flanagan, Ian T. Foster, Thomas W. Fredian, Martin J. Greenwald, Chris R. Johnson, Katarzyna Keahey, Scott A. Klasky, Kai Li, Doug C. McCune, Michael E. Papka, Q Peng, Lew Randerson, Allen Sanderson, Joshua Stillerman, Rick Stevens, Mary R. Thompson, and Grant Wallace, Building the US National Fusion Grid: results from the National Fusion Collaboratory Project, Fusion Engineering and Design, 2004 4th IAEA Technical Meeting on Control, Data Acquistion, and Remote Participation for Fusion Research

Abstract BibTeX

The US National Fusion Collaboratory Project is developing a persistent infrastructure to enable scientific collaboration for all aspects of magnetic fusion research. The project is creating a robust, user-friendly collaborative software environment and making it available to more than 1000 fusion scientists in 40 institutions who perform magnetic fusion research in the United States. In particular, the project is developing and deploying a national Fusion Energy Sciences Grid (FusionGrid) that is a system for secure sharing of computation, visualization, and data resources over the Internet. The FusionGrid goal is to allow scientists at remote sites to fully participate in experimental and computational activities as if they were working at a common site thereby creating a virtual organization of the US fusion community. The project is funded by the USDOE Office of Science, Scientific Discovery through Advanced Computing (SciDAC) Program and unites fusion and computer science researchers to directly address these challenges.

@article{ScBuFi04,
  author = {Schissel, David P. and Burruss, J.R and Finkelstein, Adam and Flanagan, S M and Foster, Ian T. and Fredian, Thomas W. and Greenwald, Martin J. and Johnson, Chris R. and Keahey, Katarzyna and Klasky, Scott A. and Li, Kai and McCune, Doug C. and Papka, Michael E. and Peng, Q and Randerson, Lew and Sanderson, Allen and Stillerman, Joshua and Stevens, Rick and Thompson, Mary R. and Wallace, Grant},
  title = {Building the US National Fusion Grid: results from the National Fusion Collaboratory Project},
  journal = {Fusion Engineering and Design},
  year = {2004},
  volume = {71},
  number = {1},
  pages = {245--250},
  note = {4th IAEA Technical Meeting on Control, Data Acquistion, and Remote Participation for Fusion Research},
  doi = {https://doi.org/10.1016/j.fusengdes.2004.04.041},
  issn = {0920-3796},
  keywords = {Fusion, Computer science, US},
  url = {https://www.sciencedirect.com/science/article/pii/S0920379604000535},
}

Nicholas T. Karonis, Michael E. Papka, Justin Binns, John Bresnahan, and Joseph M. Link, Effective use of dedicated wide-area networks for high-performance distributed computing, 2004
BibTeX
```
@techreport{KaPaBi04,
  author = {Karonis, Nicholas T. and Papka, Michael E. and Binns, Justin and Bresnahan, John and Link, Joseph M.},
  title = {Effective use of dedicated wide-area networks for high-performance distributed computing},
  year = {2004},
  institution = {Technical Report ANL/MCS-P1151-0404},
}
```
Han Gao, Ivan R. Judson, Thomas Uram, Terry L. Disz, Michael E. Papka, and Rick L. Stevens, Capability matching of data streams with network services, In IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004., 2004
BibTeX
```
@inproceedings{GaJuUr04,
  author = {Gao, Han and Judson, Ivan R. and Uram, Thomas and Disz, Terry L. and Papka, Michael E. and Stevens, Rick L.},
  title = {Capability matching of data streams with network services},
  booktitle = {IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004.},
  year = {2004},
  pages = {419--425},
  organization = {IEEE},
}
```
Wim Drongelen, Hyong C. Lee, Mark Hereld, David Jones, Matthew Cohoon, Frank Elsen, Michael E. Papka, and Rick L. Stevens, Simulation of neocortical epileptiform activity using parallel computing, Neurocomputing, 2004
BibTeX
```
@article{DrLeHe04,
  author = {van Drongelen, Wim and Lee, Hyong C. and Hereld, Mark and Jones, David and Cohoon, Matthew and Elsen, Frank and Papka, Michael E. and Stevens, Rick L.},
  title = {Simulation of neocortical epileptiform activity using parallel computing},
  journal = {Neurocomputing},
  year = {2004},
  volume = {58},
  pages = {1203--1209},
  publisher = {Elsevier},
}
```

2003

Rick Stevens, Michael E. Papka, and Terry Disz, The Access Grid: prototyping-workspaces of the future, IEEE Internet Computing, 2003
BibTeX
```
@article{StPaDi03,
  author = {Stevens, Rick and Papka, Michael E. and Disz, Terry},
  title = {The Access Grid: prototyping-workspaces of the future},
  journal = {IEEE Internet Computing},
  year = {2003},
  volume = {7},
  number = {4},
  pages = {51--58},
}
```
Aditi Majumder, David Jones, Matthew McCrory, Michael E. Papka, and Rick Stevens, Using a camera to capture and correct spatial photometric variation in multi-projector displays, In IEEE International Workshop on Projector-Camera Systems, 2003
BibTeX
```
@inproceedings{MaJoMc03,
  author = {Majumder, Aditi and Jones, David and McCrory, Matthew and Papka, Michael E. and Stevens, Rick},
  title = {Using a camera to capture and correct spatial photometric variation in multi-projector displays},
  booktitle = {IEEE International Workshop on Projector-Camera Systems},
  year = {2003},
}
```

Nicholas T. Karonis, Michael E. Papka, Justin Binns, John Bresnahan, Joseph A. Insley, David Jones, and Joseph M. Link, High-resolution remote rendering of large datasets in a collaborative environment, Future Generation Computer Systems, 2003

BibTeX

@article{KaPaBi03,
  author = {Karonis, Nicholas T. and Papka, Michael E. and Binns, Justin and Bresnahan, John and Insley, Joseph A. and Jones, David and Link, Joseph M.},
  title = {High-resolution remote rendering of large datasets in a collaborative environment},
  journal = {Future Generation Computer Systems},
  year = {2003},
  volume = {19},
  number = {6},
  pages = {909--917},
  publisher = {North-Holland},
}

Han Gao, Rick L. Stevens, and Michael E. Papka, The design of network services for advanced collaborative enviornments, In Proceedings of the 3rd Workshop on Advanced Collaborative Enviornments (WACE),(Seattle, WA), 2003
BibTeX
```
@inproceedings{GaStPa03,
  author = {Gao, Han and Stevens, Rick L. and Papka, Michael E.},
  title = {The design of network services for advanced collaborative enviornments},
  booktitle = {Proceedings of the 3rd Workshop on Advanced Collaborative Enviornments (WACE),(Seattle, WA)},
  year = {2003},
}
```

2002

W Van Drongelen, M Hereld, HC Lee, ME Papka, and RL Stevens, Simulation of neocortical activity, Epilepsia, 2002
BibTeX
```
@article{VaHeLe02,
  author = {Van Drongelen, W and Hereld, M and Lee, HC and Papka, ME and Stevens, RL},
  title = {Simulation of neocortical activity},
  journal = {Epilepsia},
  year = {2002},
  volume = {43},
  number = {Suppl 7},
  pages = {149},
}
```
Justin Binns, Michael E. Papka, Rick Stevens, Qian Peng, and David Schissel, Development of cluster-based image viewer, In High Performance Distrubuted Computing Conference. Edinburgh, Scotland, UK: IEEE CS Press, 2002
BibTeX
```
@inproceedings{BiPaSt02,
  author = {Binns, Justin and Papka, Michael E. and Stevens, Rick and Peng, Qian and Schissel, David},
  title = {Development of cluster-based image viewer},
  booktitle = {High Performance Distrubuted Computing Conference. Edinburgh, Scotland, UK: IEEE CS Press},
  year = {2002},
  pages = {450--454},
}
```
Justin Binns, Gennette Gill, Mark Hereld, David Jones, Ivan Judson, Torrance Leggett, Aditi Majumder, Matthew McCroy, Michael E. Papka, and Rick Stevens, Applying geometry and color correction to tiled display walls, In IEEE Visualization, 2002
BibTeX
```
@inproceedings{BiGiHe02b,
  author = {Binns, Justin and Gill, Gennette and Hereld, Mark and Jones, David and Judson, Ivan and Leggett, Torrance and Majumder, Aditi and McCroy, Matthew and Papka, Michael E. and Stevens, Rick},
  title = {Applying geometry and color correction to tiled display walls},
  booktitle = {IEEE Visualization},
  year = {2002},
}
```
Justin Binns, Gennette Gill, Mark Hereld, David Jones, Ivan Judson, Torrance Leggett, Aditi Majumder, Matthew McCrory, Michael E. Papka, and Rick Stevens, Applying Geometry and Color Correction to Tiled Display Walls, In IEEE Visualization 2002, 2002
BibTeX
```
@inproceedings{BiGiHe02a,
  author = {Binns, Justin and Gill, Gennette and Hereld, Mark and Jones, David and Judson, Ivan and Leggett, Torrance and Majumder, Aditi and McCrory, Matthew and Papka, Michael E. and Stevens, Rick},
  title = {Applying Geometry and Color Correction to Tiled Display Walls},
  booktitle = {IEEE Visualization 2002},
  year = {2002},
  pages = {Poster},
  organization = {IEEE},
}
```

William Allcock, Joseph Bester, John Bresnahan, Ian Foster, Jarek Gawor, Joseph A. Insley, Joseph M. Link, and Michael E. Papka, GridMapper: a tool for visualizing the behavior of large-scale distributed systems, In Proceedings 11th IEEE International Symposium on High Performance Distributed Computing, 2002

BibTeX

@inproceedings{AlBeBr02,
  author = {Allcock, William and Bester, Joseph and Bresnahan, John and Foster, Ian and Gawor, Jarek and Insley, Joseph A. and Link, Joseph M. and Papka, Michael E.},
  title = {GridMapper: a tool for visualizing the behavior of large-scale distributed systems},
  booktitle = {Proceedings 11th IEEE International Symposium on High Performance Distributed Computing},
  year = {2002},
  pages = {179--187},
  organization = {IEEE},
}

2001

Rick Stevens, Michael E. Papka, Mark Kilgard, Greg Humphreys, and Thomas Funkhouser, Commodity Graphics Accelerators for Scientific Visualization[Panel], In Proceedings of the conference on Visualization’01, 2001
BibTeX
```
@inproceedings{StPaKi01,
  author = {Stevens, Rick and Papka, Michael E. and Kilgard, Mark and Humphreys, Greg and Funkhouser, Thomas},
  title = {Commodity Graphics Accelerators for Scientific Visualization[Panel]},
  booktitle = {Proceedings of the conference on Visualization'01},
  year = {2001},
  pages = {527--529},
  location = {San Diego, California, USA},
}
```
Rick Stevens, Michael E. Papka, Chris Johnson, Pauline Baker, Jason Leigh, and Samuel Uselton, Challenges for Remote Visualization [Panel], In Proceedings of the Conference on Visualization’01, 2001
BibTeX
```
@inproceedings{StPaJo01,
  author = {Stevens, Rick and Papka, Michael E. and Johnson, Chris and Baker, Pauline and Leigh, Jason and Uselton, Samuel},
  title = {{Challenges for Remote Visualization [Panel]}},
  booktitle = {Proceedings of the Conference on Visualization'01},
  year = {2001},
  pages = {519--522},
  location = {San Diego, California, USA},
}
```
James Ahrens, Kristi Brislawn, Ken Martin, Berk Geveci, C Charles Law, and Michael E. Papka, Large-scale data visualization using parallel data streaming, IEEE Computer graphics and Applications, 2001
BibTeX
```
@article{AhBrMa01,
  author = {Ahrens, James and Brislawn, Kristi and Martin, Ken and Geveci, Berk and Law, C Charles and Papka, Michael E.},
  title = {Large-scale data visualization using parallel data streaming},
  journal = {IEEE Computer graphics and Applications},
  year = {2001},
  volume = {21},
  number = {4},
  pages = {34--41},
  publisher = {IEEE},
}
```

2000

Tushar Udeshi, Randy Hudson, and Michael E. Papka, Seamless multiresolution isosurfaces using wavelets, 2000
BibTeX
```
@techreport{UdHuPa00,
  author = {Udeshi, Tushar and Hudson, Randy and Papka, Michael E.},
  title = {Seamless multiresolution isosurfaces using wavelets},
  year = {2000},
  institution = {Argonne National Lab., IL (US)},
}
```
Robert Olson, and Michael E. Papka, Remote rendering using vtk and vic., 2000
BibTeX
```
@techreport{OlPa00,
  author = {Olson, Robert and Papka, Michael E.},
  title = {Remote rendering using vtk and vic.},
  year = {2000},
  institution = {Argonne National Lab., IL (US)},
}
```
Lisa Childers, Terrance Disz, Robert Olson, Michael E. Papka, Rick Stevens, and Tushar Udeshi, Access grid: Immersive group-to-group collaborative visualization, In 4th International Immersive Projection Technology Workshop,Jul, 2000
Abstract BibTeX

Immersive projection displays have played an important role in enabling large-format virtual reality systems such as the CAVE and CAVE like devices and the various immersive desks and desktop-like displays. However, these devices have played a minor role so far in advancing the sense of immersion for conferencing systems. The Access Grid project led by Argonne is exploring the use of large-scale projection based systems as the basis for building room oriented collaboration and semi-immersive visualization systems. The authors believe these multi-projector systems will become common infrastructure in the future, largely based on their value for enabling group-to-group collaboration in an environment that can also support large-format projector based visualization. Creating a strong sense of immersion is an important goal for future collaboration technologies. Immersion in conferencing applications implies that the users can rely on natural sight and audio cues to facilitate interactions with participants at remote sites. The Access Grid is a low cost environment aimed primarily at supporting conferencing applications, but it also enables semi-immersive visualization and in particular, remote visualization. In this paper, they describe the current state of the Access Grid project and how it relates and compares to other environments. They also discuss augmentations to the Access Grid that will enable it to support more immersive visualizations. These enhancements include stereo, higher performance rendering support, tracking and non-uniform projection surface.
```
@inproceedings{ChDiOl00,
  author = {Childers, Lisa and Disz, Terrance and Olson, Robert and Papka, Michael E. and Stevens, Rick and Udeshi, Tushar},
  title = {Access grid: Immersive group-to-group collaborative visualization},
  booktitle = {4th International Immersive Projection Technology Workshop},
  year = {2000},
  month = jul,
  address = {Ames, IA},
  url = {https://www.osti.gov/biblio/759072},
}
```
Lisa Childers, Terry Disz, Mark Hereld, Randy Hudson, Robert Olson, Ivan Judson, Michael E. Papka, Joe Paris, and Rick Stevens, ActiveSpaces on the Grid: The Construction of Advanced Visualization and Interaction Environments, In Simulation and Visualization on the Grid, 2000
Abstract BibTeX

The Futures Laboratory group at Argonne National Laboratory and the University of Chicago are designing, building, and evaluating a new type of interactive computing environment that couples in a deep way the concepts of direct manipulation found in virtual reality with the richness and variety of interactive devices found in ubiquitous computing. This environment provides the interactivity and collaboration support of teleimmersive environments with the flexibility and availability of desktop collaboration tools. We call these environments ActiveSpaces. An ActiveSpace is a physical domain that has been augmented with multiscale multi-screen displays, environment-specific and device-specific sensors, body and object trackers, human-input and instrument-input interfaces, streaming audio and video capture devices, and force feedback devices—and has then been connected to other such spaces via the Grid.
```
@inproceedings{ChDiHe00,
  author = {Childers, Lisa and Disz, Terry and Hereld, Mark and Hudson, Randy and Olson, Robert and Judson, Ivan and Papka, Michael E. and Paris, Joe and Stevens, Rick},
  editor = {Engquist, Bj{\"o}rn and Johnsson, Lennart and Hammill, Michael and Short, Faith},
  title = {ActiveSpaces on the Grid: The Construction of Advanced Visualization and Interaction Environments},
  booktitle = {Simulation and Visualization on the Grid},
  year = {2000},
  pages = {64--80},
  publisher = {Springer Berlin Heidelberg},
  address = {Berlin, Heidelberg},
  isbn = {978-3-642-57313-2},
}
```
John Bresnahan, Joseph Insley, and Michael E. Papka, Interacting with scientific visualizations: User-interface tools within spatially immersive displays, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL ANL/MCS-P789-0100, 2000
BibTeX
```
@article{BrInPa00,
  author = {Bresnahan, John and Insley, Joseph and Papka, Michael E.},
  title = {Interacting with scientific visualizations: User-interface tools within spatially immersive displays},
  journal = {Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL ANL/MCS-P789-0100},
  year = {2000},
}
```
James Ahrens, Charles Law, Will Schroeder, Ken Martin, and Michael E. Papka, A parallel approach for efficiently visualizing extremely large, time-varying datasets, Los Alamos National Laboratory, Tech. Rep.\% LAUR-00-1620, 2000
BibTeX
```
@article{AhLaSc00,
  author = {Ahrens, James and Law, Charles and Schroeder, Will and Martin, Ken and Papka, Michael E.},
  title = {A parallel approach for efficiently visualizing extremely large, time-varying datasets},
  journal = {Los Alamos National Laboratory, Tech. Rep.\\% LAUR-00-1620},
  year = {2000},
}
```

1999

Henry M. Tufo, Paul F. Fischer, Michael E. Papka, and Kris Blom, Numerical Simulation and Immersive Visualization of Hairpin Vortices, In Proceedings of the 1999 ACM/IEEE Conference on Supercomputing, 1999

BibTeX

@inproceedings{TuFiPa99c,
  author = {Tufo, Henry M. and Fischer, Paul F. and Papka, Michael E. and Blom, Kris},
  title = {Numerical Simulation and Immersive Visualization of Hairpin Vortices},
  booktitle = {Proceedings of the 1999 ACM/IEEE Conference on Supercomputing},
  year = {1999},
  pages = {62–77},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {Sc '99},
  doi = {10.1145/331532.331594},
  isbn = {1581130910},
  location = {Portland, Oregon, USA},
  url = {https://doi.org/10.1145/331532.331594},
}

HM Tufo, PF Fischer, ME Papka, and M Szymanski, Hairpin vortex formation, a case study for unsteady visualization, In 41st CUG Conference, 1999
BibTeX
```
@inproceedings{TuFiPa99b,
  author = {Tufo, HM and Fischer, PF and Papka, ME and Szymanski, M},
  title = {Hairpin vortex formation, a case study for unsteady visualization},
  booktitle = {41st CUG Conference},
  year = {1999},
  organization = {Minneapolis MN},
}
```
Henry M. Tufo, Paul F. Fischer, Michael E. Papka, and K Blom, Numerical simulation and immersive visualization of hairpin vortices, In SC’99: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing, 1999
BibTeX
```
@inproceedings{TuFiPa99a,
  author = {Tufo, Henry M. and Fischer, Paul F. and Papka, Michael E. and Blom, K},
  title = {Numerical simulation and immersive visualization of hairpin vortices},
  booktitle = {SC'99: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing},
  year = {1999},
  pages = {62--62},
  organization = {IEEE},
}
```
Lori Freitag, Terry Disz, Mike Papka, Darin Diachin, Jim Herzog, and Bob Ryan, A 3D Vector/Scalar Visualization and Particle Tracking Package, Aug, 1999
Abstract BibTeX

BOILERMAKER is an interactive visualization system consisting of three components: a visualization component, a particle tracking component, and a communication layer. The software, to date, has been used primarily in the visualization of vector and scalar fields associated with computational fluid dynamics (CFD) models of flue gas flows in industrial boilers and incinerators. Users can interactively request and toggle static vector fields, dynamic streamlines, and flowing vector fields. In addition, the user can interactively place injector nozzles on boiler walls and visualize massed, evaporating sprays emanating from them. Some characteristics of the spray can be adjusted from within the visualization environment including spray shape and particle size. Also included with this release is software that supports 3D menu capabilities, scrollbars, communication and navigation.
```
@misc{FrDiPa99,
  author = {Freitag, Lori and Disz, Terry and Papka, Mike and Diachin, Darin and Herzog, Jim and Ryan, Bob},
  title = {A 3D Vector/Scalar Visualization and Particle Tracking Package},
  year = {1999},
  month = aug,
  doi = {10.11578/dc.20180705.4},
  url = {https://www.osti.gov/biblio/1230483},
}
```
Paul F. Fischer, Michael E. Papka, Matthew Szymanski, and Henry M. Tufo, Hairpin vortex formation, a case study for unsteady visualization, Aug, 1999
Abstract BibTeX

To better understand the vortex dynamics of coherent structures in turbulent and transitional boundary layers, we consider direct numerical simulation of the interaction between a flat-plateboundary-layer flow and an isolated hemispherical roughness element. Of principal interest is the evolution of hairpin vortices that form an interlacing pattern in the wake of the hemisphere, lift away from the wall, and are stretched by the shearing action of the boundary layer. Using animations of unsteady three-dimensional representations of this flow, produced by the vtk toolkit and enhanced to operate in a CAVE virtual environment, we identify and study several key features in the evolution of this complex vortex topology not previously observed in other visualization formats.
```
@techreport{FiPaSz99,
  author = {Fischer, Paul F. and Papka, Michael E. and Szymanski, Matthew and Tufo, Henry M.},
  title = {Hairpin vortex formation, a case study for unsteady visualization},
  year = {1999},
  month = aug,
  number = {Anl/mcs/p774},
  address = {United States},
  institution = {Argonne National Laboratory},
  url = {https://www.osti.gov/biblio/11928},
}
```

1998

Michael E. Papka, Rick Stevens, and Matthew Szymanski, Collaborative virtual reality environments for computational science and design, 1998., 1998
BibTeX
```
@article{PaStSz98,
  author = {Papka, Michael E. and Stevens, Rick and Szymanski, Matthew},
  title = {Collaborative virtual reality environments for computational science and design},
  journal = {1998.},
  year = {1998},
}
```
Terrence L. Disz, Robert Olson, Michael E. Papka, Rick Stevens, Matthew Szymanski, and R. James Firby, Two implementations of shared virtual space environments, Jan, 1998
BibTeX
```
@techreport{DiOlPa98,
  author = {Disz, Terrence L. and Olson, Robert and Papka, Michael E. and Stevens, Rick and Szymanski, Matthew and Firby, R. James},
  title = {Two implementations of shared virtual space environments},
  year = {1998},
  month = jan,
  number = {Anl/mcs-p652-0297},
  address = {Lemont, IL},
  institution = {Argonne National Laboratory},
}
```

1997

Terry Disz, Michael E. Papka, and Rick Stevens, UbiWorld: an environment integrating virtual reality, supercomputing, and design, In Proceedings Sixth Heterogeneous Computing Workshop (HCW’97), 1997
BibTeX
```
@inproceedings{DiPaSt97,
  author = {Disz, Terry and Papka, Michael E. and Stevens, Rick},
  title = {UbiWorld: an environment integrating virtual reality, supercomputing, and design},
  booktitle = {Proceedings Sixth Heterogeneous Computing Workshop (HCW'97)},
  year = {1997},
  pages = {46--57},
  organization = {IEEE},
}
```

1996

Valerie E. Taylor, Jian Chen, Terrence L. Disz, Michael E. Papka, and Rick Stevens, Immersive Visualization of Supercomputer Applications: A Survey of Lag Models, Computational Science and Engineering, 1996
BibTeX
```
@article{TaChDi96b,
  author = {Taylor, Valerie E. and Chen, Jian and Disz, Terrence L. and Papka, Michael E. and Stevens, Rick},
  title = {Immersive Visualization of Supercomputer Applications: A Survey of Lag Models},
  journal = {Computational Science and Engineering},
  year = {1996},
  volume = {3},
  number = {4},
  pages = {46--54},
}
```
Valerie E. Taylor, Jian Chen, Terrence L. Disz, Michael E. Papka, and Rick Stevens, Interactive virtual reality in simulations: Exploring lag time, IEEE Computational Science and Engineering, 1996
BibTeX
```
@article{TaChDi96a,
  author = {Taylor, Valerie E. and Chen, Jian and Disz, Terrence L. and Papka, Michael E. and Stevens, Rick},
  title = {Interactive virtual reality in simulations: Exploring lag time},
  journal = {IEEE Computational Science and Engineering},
  year = {1996},
  volume = {3},
  number = {4},
  pages = {46--54},
  publisher = {IEEE},
}
```
Michael E. Papka, and Rick Stevens, Ubiworld: An environment integrating virtual reality, supercomputing and design, In Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing, 1996
BibTeX
```
@inproceedings{PaSt96,
  author = {Papka, Michael E. and Stevens, Rick},
  title = {Ubiworld: An environment integrating virtual reality, supercomputing and design},
  booktitle = {Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing},
  year = {1996},
  pages = {306--307},
  organization = {IEEE},
}
```
Ian Foster, Michael E. Papka, and Rick Stevens, Tools for distributed collaborative environments: a research agenda, In Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing, 1996
BibTeX
```
@inproceedings{FoPaSt96,
  author = {Foster, Ian and Papka, Michael E. and Stevens, Rick},
  title = {Tools for distributed collaborative environments: a research agenda},
  booktitle = {Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing},
  year = {1996},
  pages = {23--28},
  organization = {IEEE},
}
```
Terrence L. Disz, Michael E. Papka, Michael Pellegrino, and Matthew Szymanski, CAVEComm users manual, Argonne National Laboratory, Argonne, Technical Memoramum ANL/MCSTM-218, 1996
BibTeX
```
@article{DiPaPe96b,
  author = {Disz, Terrence L. and Papka, Michael E. and Pellegrino, Michael and Szymanski, Matthew},
  title = {CAVEComm users manual},
  journal = {Argonne National Laboratory, Argonne, Technical Memoramum ANL/MCSTM-218},
  year = {1996},
}
```
Terrence L. Disz, Michael E. Papka, Michael Pellegrino, and Rick Stevens, Sharing Visualization Experiences among Remote Virtual Environments, In High Performance Computing for Computer Graphics and Visualisation, 1996
Abstract BibTeX

Virtual reality has become an increasingly familiar part of the science of visualization and communication of information. This, combined with the increase in connectivity of remote sites via high-speed networks, allows for the development of a collaborative distributed virtual environ-ment. Such an environment enables the development of supercomputer simulations with virtual reality visualizations that can be displayed at multiple sites, with each site interacting, viewing, and communicating about the results being discovered.
```
@inproceedings{DiPaPe96a,
  author = {Disz, Terrence L. and Papka, Michael E. and Pellegrino, Michael and Stevens, Rick},
  editor = {Chen, M. and Townsend, P. and Vince, J. A.},
  title = {Sharing Visualization Experiences among Remote Virtual Environments},
  booktitle = {High Performance Computing for Computer Graphics and Visualisation},
  year = {1996},
  pages = {217--237},
  publisher = {Springer London},
  address = {London},
  isbn = {978-1-4471-1011-8},
}
```
Thomas A. DeFanti, Ian Foster, Michael E. Papka, Rick Stevens, and Tim Kuhfuss, Overview of the I-Way: Wide-Area Visual Supercomputing, The International Journal of Supercomputer Applications and High Performance Computing, 1996
Abstract BibTeX

This paper discusses the I-WAY project and provides an overview of the papers in this issue of IJSA. The I-WAY is an experimental environment for building distributed virtual reality applications and for exploring issues of distrib uted wide-area resource management and scheduling. The goal of the I-WAY project is to enable researchers to use multiple internetworked supercomputers and ad vanced visualization systems to conduct very large scale computations. By connecting 12 ATM testbeds, 17 supercomputer centers, 5 virtual reality research sites, and over 60 applications groups, the I-WAY project has created an extremely diverse wide-area environment for exploring advanced applications. This environment has provided a glimpse of the future for advanced scientific and engineering computing.
```
@article{DeFoPa96,
  author = {DeFanti, Thomas A. and Foster, Ian and Papka, Michael E. and Stevens, Rick and Kuhfuss, Tim},
  title = {Overview of the I-Way: Wide-Area Visual Supercomputing},
  journal = {The International Journal of Supercomputer Applications and High Performance Computing},
  year = {1996},
  volume = {10},
  number = {2-3},
  pages = {123--131},
  publisher = {Sage Publications Sage CA: Thousand Oaks, CA},
  doi = {10.1177/109434209601000201},
  eprint = {https://doi.org/10.1177/109434209601000201},
  url = {https://doi.org/10.1177/109434209601000201},
}
```
Tom Canfield, Terry Disz, Michael E. Papka, Rick Stevens, Milana Huang, Valerie Taylor, and Jian Chen, Toward real-time interactive virtual prototyping of mechanical systems: experiences coupling virtual reality with finite element analysis, In Proceedings of High Performance Computing 1996: Grand Challenges in Computer Simulation, 1996
Abstract BibTeX

Virtual prototyping involves a synthesis of engineering methodology and immersive, three-dimensional visualization technology. Ideally, this is a process in which computational models are used in place of physical models in the development of a new product or design concept. If used successfully, virtual prototyping can lead to more rapid product design and development. Software is currently being developed that will enable virtual prototyping of mechanical systems in the CAVE (CAVE Automatic Virtual Environment) at Argonne National Laboratory. This software has two principal components: (1) fast simulation software, FIFEA (Fast Implicit Finite Element Analysis), for analyzing mechanical systems and (2) virtual reality display software for visualizing results and allowing user interaction. This paper discusses various issues related to the coupling of finite element software to the CAVE display system.
```
@inproceedings{CaDiPa96,
  author = {Canfield, Tom and Disz, Terry and Papka, Michael E. and Stevens, Rick and Huang, Milana and Taylor, Valerie and Chen, Jian},
  title = {Toward real-time interactive virtual prototyping of mechanical systems: experiences coupling virtual reality with finite element analysis},
  booktitle = {Proceedings of High Performance Computing 1996: Grand Challenges in Computer Simulation},
  year = {1996},
  organization = {Society for Computer Simulation},
}
```

1995

LR Turner, D Levine, M Huang, M Papka, and L Kettunen, Using the CAVE virtual-reality environment as an aid to 3-D electromagnetic field computation, 1995
BibTeX
```
@techreport{TuLeHu95,
  author = {Turner, LR and Levine, D and Huang, M and Papka, M and Kettunen, L},
  title = {Using the CAVE virtual-reality environment as an aid to 3-D electromagnetic field computation},
  year = {1995},
  institution = {Argonne National Laboratory, Argonne, IL (United States)},
}
```
Milana Huang, Michael Papka, Thomas DeFanti, David Levine, Larry Turner, and Lauri Kettunen, Virtual reality visualization of accelerator magnets, 1995
BibTeX
```
@techreport{HuPaDe95,
  author = {Huang, Milana and Papka, Michael and DeFanti, Thomas and Levine, David and Turner, Larry and Kettunen, Lauri},
  title = {Virtual reality visualization of accelerator magnets},
  year = {1995},
  institution = {Argonne National Laboratory, Argonne, IL (United States)},
}
```
Terrence Disz, Michael E. Papka, Rick Stevens, Michael Pellegrino, and Valerie Taylor, Virtual reality visualization of parallel molecular dynamics simulation, 1995
BibTeX
```
@techreport{DiPaSt95,
  author = {Disz, Terrence and Papka, Michael E. and Stevens, Rick and Pellegrino, Michael and Taylor, Valerie},
  title = {Virtual reality visualization of parallel molecular dynamics simulation},
  year = {1995},
  institution = {Argonne National Laboratory, Argonne, IL (United States)},
}
```

Terrence L. Disz, Remy Evard, Mark W. Henderson, William Nickless, Robert Olson, Michael E. Papka, and Rick Stevens, Designing the future of collaborative science: Argonne’s futures laboratory, IEEE Parallel and Distributed Technology: Systems and Applications, 1995

BibTeX

@article{DiEvHe95,
  author = {Disz, Terrence L. and Evard, Remy and Henderson, Mark W. and Nickless, William and Olson, Robert and Papka, Michael E. and Stevens, Rick},
  title = {Designing the future of collaborative science: Argonne's futures laboratory},
  journal = {IEEE Parallel and Distributed Technology: Systems and Applications},
  year = {1995},
  volume = {3},
  number = {2},
  pages = {14--21},
  publisher = {IEEE},
}

1994

Michael E. Papka, MS Thesis: Extending genetic programming for discrete volume visualization, University of Illinois at Chicago, 1994
BibTeX
```
@phdthesis{Pa94,
  author = {Papka, Michael E.},
  title = {MS Thesis: Extending genetic programming for discrete volume visualization},
  year = {1994},
  school = {University of Illinois at Chicago},
}
```
Sumit Das, Terry Franguiadakis, Michael E. Papka, Thomas A. DeFanti, and Daniel J. Sandin, A genetic programming application in virtual reality, In Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 1994
BibTeX
```
@inproceedings{DaFrPa94,
  author = {Das, Sumit and Franguiadakis, Terry and Papka, Michael E. and DeFanti, Thomas A. and Sandin, Daniel J.},
  title = {A genetic programming application in virtual reality},
  booktitle = {Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference},
  year = {1994},
  pages = {1985--1989},
  organization = {IEEE},
}
```

1993

Carolina Cruz-Neira, Jason Leigh, Michael E. Papka, Craig Barnes, Steven M. Cohen, Sumit Das, Roger Engelmann, Randy Hudson, Trina Roy, Lewis Siegel, Christina Vasilakis, Thomas A. DeFanti, and Daniel J. Sandin, Scientists in wonderland: A report on visualization applications in the CAVE virtual reality environment, In Proceedings of 1993 IEEE Research Properties in Virtual Reality Symposium,Oct, 1993
Abstract BibTeX

The authors present the experiences at the Electronic Visualization Laboratory (EVL) in introducing computational scientists to the use of virtual reality as a research tool. They describe the virtual environment, the CAVE. They then describe several applications currently being developed at EVL using the CAVE and conclude with a discussion on possible research paths to follow in making virtual reality an effective tool for visualization.
```
@inproceedings{CrLePa93,
  author = {Cruz-Neira, Carolina and Leigh, Jason and Papka, Michael E. and Barnes, Craig and Cohen, Steven M. and Das, Sumit and Engelmann, Roger and Hudson, Randy and Roy, Trina and Siegel, Lewis and Vasilakis, Christina and DeFanti, Thomas A. and Sandin, Daniel J.},
  title = {Scientists in wonderland: A report on visualization applications in the CAVE virtual reality environment},
  booktitle = {Proceedings of 1993 IEEE Research Properties in Virtual Reality Symposium},
  year = {1993},
  month = oct,
  pages = {59--66},
  doi = {10.1109/vrais.1993.378262},
}
```