Michael E. Papka

selected publications

1. 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, 2023

   Abs arXiv Bib

   The ability to monitor and interpret of hardware system events and behaviors are 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 multiresolution 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.

   @misc{ShLuEm23,
     archiveprefix = {arXiv},
     author = {Shilpika and Lusch, Bethany and Emani, Murali and Simini, Filippo and Vishwanath, Venkatram and Papka, Michael E. and Ma, Kwan-Liu},
     eprint = {2306.09457},
     primaryclass = {cs.HC},
     title = {A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems},
     year = {2023}
   }

2. 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

   Abs Bib

   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,
     address = {New York, NY, USA},
     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},
     booktitle = {Practice and Experience in Advanced Research Computing},
     doi = {10.1145/3569951.3593597},
     isbn = {9781450399852},
     keywords = {FAIR Data, Modern Research Data Portal, Globus},
     location = {Portland, OR, USA},
     numpages = {9},
     pages = {43–51},
     publisher = {Association for Computing Machinery},
     series = {PEARC '23},
     title = {Active Research Data Management with the Django Globus Portal Framework},
     url = {https://doi.org/10.1145/3569951.3593597},
     year = {2023}
   }

3. Boyang Li, Yuping Fan, Michael E. Papka, and Zhiling Lan, Encoding for Reinforcement Learning Driven Scheduling, In Job Scheduling Strategies for Parallel Processing, 2023

   Abs Bib

   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,
     address = {Cham},
     author = {Li, Boyang and Fan, Yuping and Papka, Michael E. and Lan, Zhiling},
     booktitle = {Job Scheduling Strategies for Parallel Processing},
     editor = {Klus{\'a}{\v{c}}ek, Dalibor and Julita, Corbal{\'a}n and Rodrigo, Gonzalo P.},
     isbn = {978-3-031-22698-4},
     pages = {68--87},
     publisher = {Springer Nature Switzerland},
     title = {Encoding for Reinforcement Learning Driven Scheduling},
     year = {2023}
   }

4. Bharat Kale, Maoyuan Sun, and Michael E. Papka, The State of the Art in Visualizing Dynamic Multivariate Networks, Computer Graphics Forum, 2023

   Abs Bib

   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.},
     doi = {https://doi.org/10.1111/cgf.14856},
     journal = {Computer Graphics Forum},
     keywords = {CCS Concepts, • Human-centered computing —> Graph drawings},
     number = {3},
     pages = {471-490},
     title = {The State of the Art in Visualizing Dynamic Multivariate Networks},
     url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14856},
     volume = {42},
     year = {2023}
   }

5. 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

   Abs Bib

   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.},
     doi = {https://doi.org/10.1111/cgf.14807},
     journal = {Computer Graphics Forum},
     keywords = {chemical space exploration, cheminformatics, multipartite graphs, data visualization, CCS Concepts, • Applied computing → Chemistry},
     number = {3},
     pages = {13-24},
     title = {ChemoGraph: Interactive Visual Exploration of the Chemical Space},
     url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14807},
     volume = {42},
     year = {2023}
   }