EVL Director Maxine Brown of UIC Computer Science, PI, and co-PIs Andrew Johnson, Robert Kenyon and G. Elisabeta Marai received a three-year NSF MRI award entitled &ldquo;Acquisition of a Composable Platform as a Service Instrument for Deep Learning &amp; Visualization (COMPaaS DLV).&rdquo; Senior Personnel are from several COE departments: Tanya Berger-Wolf (CS), Sybil Derrible (CME), Barbara Di Eugenio (CS), Chris Kanich (CS), Alex Leow (BioEng/Psychiatry), Bing Liu (CS), Lance Long (EVL), Farzad Mashayek (MIE), Roberto Paoli (MIE), Luc Renambot (CS), Philip Yu (CS), Milos Zefran (ECE), Xinhua Zhang (CS), and Brian D. Ziebart (CS). 
The COMPaaS DLV system, being designed by EVL staff and faculty, will be based on a new computer architecture (&ldquo;composable infrastructure&rdquo), and will focus on large-scale (Big Data) deep learning and visualization problems. It will be a resource for COE faculty and students to use for research and research training. 
NSF Award ID: CNS-1828265 
Project Period: 10/1/18-9/30/21 
Award Amount: $997,363 
Abstract: 
This project is about acquiring a much-in-demand Graphics Processing Unit (GPU)-based instrument, to develop a Service Instrument for Deep Learning and Visualization called &ldquo;COMPaaS DLV: COMposable Platform&rdquo;. The project aims to complement available campus computing resources via the campus&rsquo;s research network. It will be able to access local and remote computing and storage facilities funded, including XSEDE, Blue Waters, Deep Learning Instrument and Chameleon. This critical instrumentation will provide a platform to pursue fundamental science and engineering research training in deep learning (data mining and data analytics, computer vision, natural language processing, artificial intelligence), visualization (simulation, rendering, visual analytics, video streaming, image processing), and a combination of deep learning and visualization (e.g., when data is so large that it cannot be easily visualized, then deep learning is used to extract features of interest to be visualized). The instrumentation also enables investigation and contribution to societal issues in disciplines such as anthropology, biology, cybersecurity, data-literacy, fraud detection, healthcare, manufacturing, urban sustainability, and cyber-physical systems (e.g., autonomous cars). 
Its design utilizes state-of-the-art computer architecture, known as composable architecture, in which the computer&rsquo;s components (traditional processor, GPU, storage, and networking) form a fluid pool of resources, such that different applications with different workflows can be run simultaneously, with each configuring the resources it requires almost instantaneously, at any time. Given composable infrastructure scalability and agility, it is more beneficial than traditional clouds and clusters that are rigid, overprovisioned, and expensive. 
See <a href="https://today.uic.edu/nsf-grant-to-fund-advanced-deep-learning-and-visualization-computing-platform">UIC Today News Release</a> for more information.

An application that will benefit from COMPaaS DLV is “SENSEI Panama,” which applies Deep Learning and Visualization to help anthropologists walk inside their data using virtual reality to study animal behavior.

&ldquo;Acquisition of a Composable Platform as a Service Instrument for Deep Learning &amp; Visualization (COMPaaS DLV)&rdquo; Awarded

Project Period: October 1, 2018 - September 30, 2021 
September 9, 2018 - The National Science Foundation (NSF) awarded a three-year grant to the UIC Computer Science (CS) Department&rsquo;s Electronic Visualization Laboratory (EVL) to acquire the &ldquo;Composable Platform as a Service Instrument for Deep Learning &amp; Visualization (COMPaaS DLV)&rdquo;. The total project cost is $1,424,806, with NSF providing $997,363 for hardware and the balance provided by UIC cost share to cover staff and graduate student support to design, operate and maintain the system. Maxine Brown, EVL Director, is Principal Investigator (PI) and co-PIs are CS faculty and EVL affiliated faculty members Andrew Johnson, Robert Kenyon and G. Elisabeta Marai. 
COMPaaS DLV will be acquired and made available for Deep Learning and Visualization research and training. Initial users will be 17 faculty from 5 departments in the UIC College of Engineering (COE) who provided compelling use cases for this system, and then expanded to other COE faculty as availability and demand permits. Users include the above-mentioned PI and co-PIs, plus COE Senior Personnel: Tanya Berger-Wolf (CS), Sybil Derrible (Civil &amp; Materials Eng.), Barbara Di Eugenio (CS), Chris Kanich (CS), Alex Leow (BioEng/Psychiatry, and by courtesy CS), Bing Liu (CS), Farzad Mashayek (Mechanical &amp; Industrial Eng.), Roberto Paoli (Mechanical &amp; Industrial Eng.), Luc Renambot (CS), Philip Yu (CS), Milos Zefran (Electrical &amp; Computer Eng.), Xinhua Zhang (CS), and Brian D. Ziebart (CS). 
COMPaaS DLV is critical infrastructure on which UIC faculty can pursue fundamental research in deep learning (data mining, data analytics, computer vision, natural language processing, artificial intelligence), visualization (simulation, rendering, visual analytics, video streaming, image processing), and a combination of deep learning and visualization (that is, when data is so large that it cannot be easily visualized, deep learning is used to extract features of interest to be visualized). UIC faculty are investigating, and contributing to, solutions for societal issues in such areas as anthropology, biology, cybersecurity, data literacy, fraud detection, healthcare, manufacturing, urban sustainability, and cyber-physical systems (e.g., autonomous vehicles). 
Though EVL specializes in visualization and virtual reality, it has a longstanding reputation as a technology enabler for the campus. EVL will design and operate COMPaaS DLV as a highly flexible, scalable, high-throughput system for Big Data. Its flexible 4th-generation computer architecture, &ldquo;composable infrastructure,&rdquo; treats computer components - computer processing units (CPUs), graphical processing units (GPUs), and storage - as pools of resources. Each application defines what resources it needs, and the operating system composes, or combines, resources on the fly and infrastructure is provisioned &lsquo;right-sized&rsquo; and resized as needed. The system is easily scalable - new infrastructure is automatically integrated with existing infrastructure and becomes part of the pool of capacity. The system&rsquo;s components are all interconnected with a high-speed internal network fabric, so that Big Data can quickly move between CPU, GPU and storage at optimum speed, i.e., high-throughput, with little to no bottlenecks. 
COMPaaS DLV will complement, but be connected via UIC&rsquo;s research network, to campus and regional clusters, data storage, supercomputing and cloud testbed facilities managed by the UIC ACCC&rsquo;s Advanced Cyberinfrastructure for Education and Research (ACER) group, the National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign, Argonne National Laboratory&rsquo;s Leadership Computing Facility, and University of Chicago. 
Access to COMPaaS DLV will enable faculty, students and postdocs to execute deep learning and visualization codes faster, apply more sophisticated models to large-scale problems, gain greater insights, accelerate discovery, and open new avenues of research. The instrument will enable UIC to recruit new faculty who are attracted to institutions that can support their computational needs, and to recruit new students and produce a better trained workforce - which in turn attracts industrial partnerships. 
See <a href="https://today.uic.edu/nsf-grant-to-fund-advanced-deep-learning-and-visualization-computing-platform">UIC Today News Release</a> for more information. 
PHOTO CAPTION: One of the applications that will benefit from COMPaaS DLV is &ldquo;SENSEI Panama,&rdquo; a UIC collaborative project with evolutionary anthropologist Meg Crofoot (UC Davis) to enable anthropologists to use virtual reality to &lsquo;walk inside their data&rsquo; and study animal behavior. Specifically Crofoot and UIC computer scientists Tanya Berger-Wolf, Andy Johnson and Maxine Brown, are applying Deep Learning and Visualization to process data from LIDAR, aerial photography, terrain maps, GPS collars, and accelerometers, in order to study how coatis, kinkajous, spider monkeys, and capuchin monkeys on Barro Colorado Island (a Smithsonian Tropical Research Institute Research Center) in Panama, forage for food. This project received some funding from the NSF SENSEI $3.5M award to UIC CS Maxine Brown (principal investigator) and UIC CS co-PIs Andy Johnson, Tanya Berger-Wolf, and Robert Kenyon. (This project/photo won Honorable Mention in the <a href="https://www.evl.uic.edu/entry.php?id=2242">UIC 2016 Image of Research Competition</a>).

An application that will benefit from COMPaaS DLV is &ldquo;SENSEI Panama,&rdquo; which applies Deep Learning and Visualization to help anthropologists walk inside their data using virtual reality to study animal behavior.

COMPaaS DLV: Composable Platform as a Service Instrument for Deep Learning & Visualization

Umwelt, the German word for environment, is a psychology term referring to the individual&rsquo;s mental image of the surrounding world. This subjective universe is shaped by an organism&rsquo;s unique perception of the environment around them and, therefore, differs from individual to individual. In other words, organisms experience the world through their senses, construct the mental model of their vicinity, and react upon it. Hence, this image never remains static - instead it is constantly refined, as a result of the dynamic nature of the world. Umwelt is closely related to the concept of Situation Awareness, that is the perception of environmental elements, comprehension of their nature, and projection of their near future status with respect to time and/or space. 
Sensory perception (exteroception) of the surrounding environment is, as a matter of fact, restricted to the limitations of biological senses. For example, although we consider humans with five fully developed senses (sight, hearing, touch, smell and taste) as able-bodied individuals, they are essentially blind to a multitude of information that constantly surrounds them. To put things into perspective, both human sight and hearing, can only perceive a fraction of information- part of the electromagnetic spectrum (light waves), and of the sound frequencies accordingly. However, as sensory systems differ among species, other creatures have an entirely different perceptual experience, as they are able to perceive a distinct spectrum of information. 
Despite the fact that our limited human senses restrict our Umwelt and, therefore, our Situation Awareness, Human Augmentics, referring to technologies that expand the capabilities and characteristics of humans, allows us to peek into an invisible &dlquo;other&drquo; world. Thus, some important questions arise: What would the consequences of an expanded perception of the
surrounding environment be, and how would that affect an individual&rsquo;s Situation Awareness? Since our Umwelt is limited by genetics, how would individuals attune with characteristics and capacities that lie beyond human range? Could Human Augmentics possibly expand an individual&rsquo;s Umwelt, leading to improved Situation Awareness? 
Jakob von Uexkull (1864 - 1944), a German biologist who worked on animal behavior studies, cybernetics of life, and muscular physiology, is attributed for coining the term Umwelt. Uexkull&rsquo;s Umwelt is the phenomenal world of a living animal, in which it exists and acts, and as a result is shaped according to the stimuli received from its senses. Due to the fact that individuals, even across the same species, sense the world differently, each animal creates its own unique Umwelt. 
Perception and, as a result, interaction with the world begins with the raw input of data from the environment through the sensory system. Anatomically, the sensory system is part of the nervous system and includes the sensory receptors that act as probes of the environment, parts of the brain that are used for sensory perception, and the neural pathways that connect the brain with the receptors. Notably, humans&rsquo; sensory system consists of five traditional senses: Sight, hearing, taste, smell and touch. The stimulation type and bandwidth of each sensory channel are limited to its characteristics, which in turn constrain the size of the Umwelt.
However not all living organisms possess the same qualitative or quantitative senses. Depending on the environment in which they have evolved, their sensory system may be entirely different than ours; some have more while others have less sensitive senses. Others have quite different ones. Bats and dolphins, for instance, have the ability to calculate orientation and position of other animals and objects through reflected beams of sound, an ability known as echolocation. Sharks and rays, on the other hand, can detect changes in the electric field of their vicinity, an ability known as electroreception. 
Situation Awareness 
It is a well-known fact that humans are capable of sensing only a small fraction of the data that constantly surrounds us. Magnetic fields, infrared light, ultrasound, cosmic rays, radiation are just a few of the things that are completely invisible to us. However, recent technological advances in specialized sensors, enable the detection of some of this information. For instance, geiger counters are used by hazmat personnel to assess radiation levels in nuclear accidents. These technologies provide feedback using one of our existing senses, usually visual or audio, in order to help workers safely perform daily tasks. 
Situation Awareness (SA) is the perception of environmental elements, comprehension of their nature, and projection of their near future status with respect to time and/or space. In other words, it is the awareness of the immediate vicinity, comprehension of the current state of the environment and ability to predict what may happen. Inputs from the sensory system are used by the individual in order to create and update their Situation Awareness state, and as a result, decisions depend on the accuracy of the perceived model. Situation Awareness is tightly associated with experience; somebody less experienced may miss information that a more experienced user will not. To put things into perspective, the hazmat personnel&rsquo;s (that we discussed above) Situation Awareness includes the geiger counter readings, comprehension of their meaning and ability to predict what will happen if they raise above a certain level. 
Despite the fact that sensory systems have been honed by millions of years of evolution, they are very limited to a multitude of data. We are blind to a plethora of information that exists around us, and fortunately most of it is harmless. We depend on our sensory system to interpret the world, and we use our Situation Awareness to comprehend the environment around us and act upon it. As a result, lack of information for a certain situation may lead to misjudgements and accidents. In fact 76% of aviation errors, happen because the pilot did not perceive one piece of the airplane&rsquo;s crucial information. To that end, another important question arises: Given that today&rsquo;s technology can sense some of this invisible information, can we expand the Umwelt and improve Situation Awareness? 
Research Questions 
Genetic defects, diseases or accidents can lead to sense disabilities or complete sense lose, which has been observed to both animals and humans. To that end, individuals learn over time to use the remaining of their senses in order to overcome a disability. Recent scientific discoveries have shown that while this new ability results in anatomical changes of the brain, the responsible regions remain the same. In other words, even though the sensory input channel has changed, the brain will still process the information in the same region|a phenomenon known as Sensory Substitution. 
As a result, Sensory Substitution devices use one or more of the remaining senses to increase an individual&rsquo;s Situation Awareness through a different sensory pathway. More specifically, tactile displays are able to translate external environmental information onto one&rsquo;s skin, through the use of haptics. While these systems are primarily designed for people with disabilities, their concepts can be extended to other sensory deprived individuals, who find themselves in no or limited visibility environments due to external, environmental reasons. For example, firefighters operating within smoky, low visibility hazardous environments or soldiers fighting in pitch black darkness, are more likely to have no Situation Awareness of their surrounding environment. 
The ever-dynamic nature of the world around us, however, makes navigating with a wearable tactile display, a quite challenging task. From feeling the vibrations onto one&rsquo;s skin, comprehending their meaning and correctly acting upon them, to getting inundated with tactile sensory overload, tactile displays have great potential, when communicating information in an eloquent
way. 
In this dissertation we will explore different environment-to-vibration mappings while assessing one&rsquo;s Situation Awareness. Furthermore, we seek to understand whether one's experience with a tactile display would have an effect on their Situation Awareness. Finally, we hypothesize that a one-size-fits-all wearable tactile display would not perform equally well in all environmental scenarios and therefore we are going to explore how different vibration configurations, patterns and environment-to-vibration mappings would affect one&rsquo;s navigation. All of the above are distilled into the following research questions: 
- Could tactile displays be used to increase Situation Awareness of visually deprived individuals? 
- To what extent would Situation Awareness be associated with experience when using a tactile display?

Positioning of Sensor Modules and Controller Box

Supporting Navigation with a Torso Wearable Tactile Display

This article provides a 25 year-long perspective on Immersive Analytics through the lens of first-in-kind technological advancements introduced at the Electronic Visualization Laboratory, University of Illinois at Chicago, along with the challenges and lessons learned from multiple Immersive Analytics projects. 
The Electronic Visualization Lab (EVL) at the University of Illinois at Chicago has been at the forefront of Virtual Reality research since 1992, when it introduced the CAVE&trade; Automatic Virtual Environment, the first projection-based virtual reality system in the world. Since then, the lab has developed a wide range of immersive systems and technologies to be used for visualization and visual analysis tasks, such as PARIS&trade;, ImmersaDesk&trade;, CAVE2&trade;, and SAGE2&trade;. These technologies paved the way for the resurgence in virtual reality in the past few years, and lead to the introduction of Immersive Analytics - which investigates how new interaction
and display technologies can be used to support analytical reasoning by immersing the users in data.

CAVE2 immersive exploration of dark matter formation in the universe. While little information can be shown on a typical desktop display, the high-resolution tiled environment lets analysts examine details without losing the context of the larger dataset.

Immersive Analytics Lessons from the Electronic Visualization Laboratory: a 25 Year Perspective

A system and methods for visualizing information on a generally large scale. Certain embodiments include display elements that include a minimized border margin, 2D/3D dual functionality, and ultra-high resolution. The display elements may be positioned relative to an infrastructure element configured to permit generating a wide variety of display geometries. 
This patent resulted in realizing the CAVE2 Hybrid Reality Environment.

Illustration of user viewing an embodiment of a patterned-retarded barrier layer shifted adjacent to a layer of pixels from a side view.

System and Methods for Visualizing Information (US 9,800,862 B2 - CAVE2)

Research Experience Undergraduate (REU)

Szymon Popowski

The main focus of this research is to evaluate the combination of the CAVE2&trade; and the HTC VIVE to improve the collaborative tasks. The thesis describes background work conducted as part of an internship with Mechdyne, Inc., which provides the groundwork for the core research. 
We are living in a very interesting period for virtual reality (VR). The general press called 2016 &ldquo;the year of the VR&rdquo;. In fact, they talked about the new Head Mounted Display (HMD) being available for the first time to the general public. They manage to produce advances in HMD and making it more affordable (such as HTC, Oculus, and Sony). VR exists since the 1950&rsquo;s and takes different forms other than an HMD. For example, the CAVE&trade; is a VR device. Its screens are not in front of the eyes of the user - rather it is projected on large screens. While HMD is the opposite. CAVE is very expensive to manufacture and requires big spaces. This type of VR is more fitting for industrial use. On the other hand, HMD is also suitable for industrial use because it has high-quality engagement among participants, it is less expensive to produce, and it is at parity with CAVE&rsquo;s standard. Both devices have advantages and disadvantages. Thus, it would be interesting to combine both devices to complement each other. 
VR has a lot of applications. One of the applications that arouses great interests among companies is its collaborative application. It connects people in various locations to work together. With the advent of globalization, many big companies have an international presence and their subsidiaries are located in various countries. In order to bring them together, they need an effective network of communication. Essentially, companies such as Microsoft with Windows Holographic or MiddleVR with Improov3 are developing their own platforms and devices to connect people in various locations in a more efficient way. 
With this in mind, the objective of this thesis works is to develop a synergy among people by combining two VR devices to explore the efficiencies and capabilities. 
Keywords: HTC VIVE, CAVE2, collaboration, NASA TLX, Virtual Reality, EVL, Mechdyne, HoloLens, OmegaDesk, Cyber-Commons, Unity, interaction, HMD, Oréal, virtual shopper

Virtual Shopper VIVE application.

Telecollaboration Task Between CAVE2 and HTC VIVE

COMPaaS DLV, COMposable Platform as a Service Instrument for Deep Learning & Visualization, was purchased from Liqid, Inc., with NSF funds. Its 4th-generation computer architecture, composable infrastructure, treats computer components - computer processing units (CPUs), graphical processing units (GPUs), networking, memory and storage - as pools of resources. It is: 
<ul><li>Highly flexible. Each application defines the resources it needs, and the operating system composes, or combines, resources on the fly.</li> 
<li>Scalable. As more infrastructure is added, it’s auto-integrated with the existing infrastructure and becomes part of the pool of capacity.</li> 
<li>High-throughput. System components are interconnected with a high-speed internal fabric, enabling Big Data to quickly move among CPU, GPU, memory and storage at optimum speeds with little to no bottlenecks.</ul></li> 
Funded in part by NSF award #CNS-1828265.

Visual Data Science: Advanced Cyberinfrastructure Systems (poster session)

Led by EVL Director Maxine Brown, a large team of research faculty and staff from UIC&rsquo;s College of
Engineering applied for and received a Major Research Instrumentation (MRI) grant from the National Science Foundation (NSF) for approximately $1-million to acquire a deep learning and visualization infrastructure. 
The design utilizes composable infrastructure architecture from <a href="https://www.liqid.com/">Liqid</a>. The computer&rsquo;s components (traditional processor, GPU, storage, and networking) are pooled so that different applications with different workflows can be run simultaneously, with each configuring the resources it requires almost instantaneously, at any time. 
&ldquo;EVL resources are traditionally very tailored to meet the requirements of specific applications. For example, on any given day, EVL may address everything from computational fluid dynamics, ecology and neuroimaging to natural language processing, urban engineering, robotics, visualization and visual analytics,&rdquo; said Lance Long, EVL Senior Research Programmer. &ldquo;Liqid&rsquo;s composable infrastructure architecture is interesting because it allows us the flexibility to redefine cyberinfrastructure programmatically based on the applications&rsquo; requirements. This enables the EVL team to create a data science platform that presents a full-stack hardware and software solution to the challenges faced by our researchers trying to deploy and scale reproducible experiments.&rdquo;

EVL&rsquo;s COMPaaS DLV system

Case Study: UIC Utilizes Liqid Composable Infrastructure for Uneven Applications in Scientific Research

In October 2018, the UIC Electronic Visualization Laboratory (EVL) received an equipment grant from the National Science Foundation (NSF #1828265) for a GPU-based system for College of Engineering research and education. Since that time, EVL staff has surveyed vendors, analyzed UIC faculty&rsquo;s application requirements, and then designed, purchased, received and installed its GPU-based system from Liqid. EVL refers to its new computing system as Composable Platform as a Service Instrument for Deep Learning & Visualization (COMPaaS DLV). 
<a href="https://www.liqid.com/">Liqid</a> is a start-up company specializing in systems integration (and has major industrial partners such as Dell, Intel, Nvidia and Mellanox), who currently sells its next-generation &ldquo;composable infrastructure&rdquo; computer systems primarily to data centers. Working with UIC faculty, it became apparent that this advanced architecture could address their diverse application requirements as their data moves through the computational pipeline (e.g., data gathering, filtering, computing, storing). 
EVL worked with Liqid to educate them about how its product could be tailored to meet the needs of the academic research market, helping them realize that science and engineering research is a potential market for their product. Working together, Liqid has configured a system to suit the needs of EVL&rsquo;s research focus. 
Liqid has published a <a href="https://www.evl.uic.edu/entry.php?id=2495">case study, &ldquo;UIC Utilizes Liqid Composable Infrastructure for Uneven Applications in Scientific Research&rdquo;</a> that explores EVL&rsquo;s use of COMPaaS DLV.

Liqid publishes a case study on how EVL is using their GPU-based system COMPaaS DLV

October 1 - - EVL Director of Research Andrew Johnson has been named Interim Director of EVL following Maxine Brown&rsquo;s retirement from UIC. Johnson now assumes the responsibility of heading EVL - overseeing EVL&rsquo;s overall mission in education, research, development, and technology transfer. 
For over 23 years, Johnson has been a valued EVL faculty member (Professor of Computer Science) and researcher who has focused upon interaction and collaboration using advanced visualization displays and the application of those displays to enhance discovery and learning. 
His areas of expertise include visualization, virtual and augmented reality, natural language processing, visual analytics, and high-performance networking. 
Over the years, Johnson has been instrumental in the development of EVL instrumentation and systems, including the Continuum smart environment, CAVE2&trade;, LambdaVision high-resolution tiled displays, LambdaTable tabletop touch system, GeoWall portable geoscience display environment, PARIS augmented reality and ImmersaDesk virtual reality systems. He has overseen the development of numerous visualization and virtual reality applications across diverse domain disciplines from science and engineering to the humanities and art. Johnson is a highly regarded mentor and educator to EVL&rsquo;s graduate students and undergraduates alike. 
Johnson retains his position as Director of Research while leading EVL&rsquo;s future initiatives and innovations as Interim Director.

Andrew Johnson Named Interim Director, EVL

Large high-resolution displays are becoming increasingly common in research settings, providing data scientists with visual interfaces for the analysis of large datasets. Numerous studies have demonstrated unique perceptual and cognitive benefits afforded by these displays in visual analytics and information visualization tasks. However, the effects of these displays on knowledge discovery in exploratory visual analysis are still poorly understood. We present the results of a small-scale study to better understand how display size and resolution affect insight. Analyzing participants&rsquo; verbal statements, we find preliminary evidence that larger displays with more pixels can significantly increase the number of discoveries reported during visual exploration, while yielding broader, more integrative insights. Furthermore, we find important differences in how participants performed the same visual exploration task using displays of varying sizes. We tie these results to extant work and propose explanations by considering the cognitive and interaction costs associated with visual exploration.

An overview map (left) and a compound view showing one years worth of criminal activity (a) for a particular type of crime (b). The heatmap shows the aggregate spatial distribution of crime over the year.

Effects of Display Size and Resolution on User Behavior and Insight Acquisition in Visual Exploration

Construction activities for the new $117.8 million, 135,000-square-foot Computer Design Research and Learning Center to be located at 900 W. Taylor St. is underway. The CDRLC will support the Department of Computer Science&rsquo;s growing student enrollment with 16,000 square feet of classroom space for collaborative teaching and learning environments, as well as 35 labs for faculty research activities in the computer science and engineering disciplines. 
The CDRLC will be the new home for the Electronic Visualization Laboratory featuring the latest technological and computing innovations to support research in advanced visualization, networking and collaborative environments. 
Read more about the CDRLC groundbreaking in the <a href="https://today.uic.edu/uic-breaks-ground-on-new-computer-design-research-and-learning-center">July 15th issue of UIC Today.</a> 
See a live webcam of the building&rsquo;s progress via the <a href="https://webcampub.multivista.com/index.cfm?fuseaction=aPublicWebcam.embed&WebcamPublicEmbedUID=5D4DCF05-AB2B-4770-A090-D79BE467F958">Multivista Construction Documentation webcam.</a>

New Computer Design Research and Learning Center Breaks Ground

Long-time EVLer and PhD candidate Arthur Nishimoto has been recognized by the UIC College of Engineering with the 2022 Exceptional Research Promise Award.  Arthur began his work with EVL in 2009 as a Research Experience Undergraduate developing virtual reality and visualization applications for collaborative research between EVL and NASA.  After graduating with his bachelorette degree, Arthur pursued a MS in computer science with focus on advanced visualization, virtual reality and real-time interactive application development.  Since 2014, Arthur has been working towards his PhD (expected Summer 2022), has been an incredibly prolific and valued member of EVL.  Arthur is well-deserving of this honor.  We look forward to seeing him continue his exceptional research over his career.

EVL PhD Candidate Arthur Nishimoto Recognized with the COE Exceptional Research Promise Award

EVL is a technology Innovator, building industry and cross-institutional connections and partnerships. In 2022, EVL deployed two GPU FIONAs in partnership with the Pacific Research Platform (PRP) and the University of California San Diego. The PRP is a partnership of more than 50 institutions that establishes a science-driven high-capacity data-centric &lsquo;freeway system&rsquo;. PRP has support from the National Science Foundation, and includes the US Department of Energy&rsquo;s Energy Science Network (ESnet), as well as scores of research universities in the nation and around the world. PRP leverages Nautilus, a HyperCluster for running containerized Big Data Applications using Kubernetes, and FIONA, flash input/output network applications. A GPU FIONA is a rack-mounted PCs designed by the PRP team that is optimized for 100Gbps large data storage with Mellanox ConnectX-6 SmartNICs, a 24 core Epic Rome CPU, 256GB of memory and 8 NVIDIA RTX-3090 GPUs. Nautilus provides over 7500 CPU cores, over 500 GPUs and 4 PBs of storage. The addition of two Nautilus GPU FIONAs at EVL builds upon our 2018 DTN (Data Transfer Node) that has provided over 300GB of Ceph Storage to Nautilus and our CAVE2 GPU Nodes that are contributing 5 NVIDIA GPUS. EVL provides Nautilus with 300GB of Ceph Storage, 128 CPU cores, and 21 NVIDIA GPUs. The platform is utilized for a wide variety of applications, including applying advanced astronomy AI to microscopy workflow, simulations for natural disasters, climate modeling, virtual reality and even accelerating NSF&rsquo;s IceCube Photon Propagation. UIC is enabled to participate in these cross-institutional connections using UIC&rsquo;s HPRNet, a network environment tailored to the needs of high-performance science applications, built on a Science DMZ architecture. 
<a href="https://pacificresearchplatform.org/media/">Read more about PRP.</a> 
<a href="https://pacificresearchplatform.org/media/sc21-larry-smarr-on-the-rise-of-supernetwork-data-intensive-computing/">See media coverage from SC21 on PRP and Nautilus.</a>

FIONA

Pacific Research Platform (PRP) - Nautilus

The Electronic Visualization Laboratory at the University of Illinois Chicago acquired a 24 compute node, 64 GPU composable infrastructure compute cluster, COMPaaS DLV - Composable Platform as a Service: Instrument for Deep Learning & Visualization in 2019.  Since then 40+ users have designed and run computations on COMPaaS.  To track cluster utilization, evaluate hardware
performance, and simply analyze application resource utilization of this experimental composable infrastructure system we set out to
collect metrics on the hardware and software infrastructure.  Users deploy their applications on the cluster using Kubernetes, a container orchestration platform that enables users to deploy applications on the cluster without the need to have complete knowledge of the underlying hardware and with minimal assistance from system administrators.  Following this, we utilized tooling already adapted for Kubernetes-based clusters to collect hardware and software metrics.  The metrics provided by this tooling is useful for system administrators in evaluating cluster performance and utilization, as well as useful for users in determining their application&rsquo;s performance and discovering bottlenecks.

Instrumentation

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