ACM named an eight-member team drawn from Australian and American institutions as the winner of the 2024 ACM Gordon Bell Prize for the project, “Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials.”

The members of the team are Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C.Y. Yu, Calum Snowdon, Elise Palethorpe (all of Australian National University); Jakub Kurzak (Advanced Micro Devices, Inc.); Dmytro Bykov (Oakridge National Laboratory); and Giuseppe M.J. Barca (University of Melbourne).

Molecular dynamics is a computer simulation method that has been developed to better understand the movements of atoms and molecules within a system. Among the different approaches taken are Ab Intio (or first principles) calculations, whereby scientists use what is known of the fundamental laws of nature to develop the algorithms they run on computers.

Accurate simulations of the properties of molecules and atoms (as well as how they interact) can lead to a wide range of societal benefits, including developing therapeutic drugs, producing biofuels, recycling plastics, and engineering medical biomaterials.

Although the use of computers for performing molecular dynamics simulations goes back several decades, many traditional approaches have been limited by the accuracy of the force fields (computational models computer scientists develop to describe the forces between atoms and molecules). These limits have undermined the accuracy of the resulting simulations.

While other approaches such as quantum mechanical methods have delivered the desired accuracy, they were not able to scale on powerful supercomputers to model the thousands of atoms within a biosystem.

To address this challenge, the Gordon Bell Prize-winning team developed a new technique combining methods called molecular fragmentation and MP2 perturbation theory.

Using their algorithmic innovations on a powerful exascale computer, the team was able to achieve a record-breaking performance of simulating more than one million electrons for a computational chemistry application, and to scale their algorithm to an EFlop/s (processing a quintillion calculations per second). The Gordon Bell Prize-winning team’s resulting simulation is 1,000 times larger in system-size than the existing state-of-the-art, and was processed 1,000 times faster than any previous model.

The Gordon Bell Prize-winning team performed several Ab Initio Molecular Dynamics (AIMD) time steps on a molecular cluster with over two million electrons utilizing 9,400 nodes on the Frontier exascale supercomputer, significantly larger than any previous AIMD or static energy and/or gradient calculation at a comparable level of accuracy. These calculations achieve 1006.7 PFLOP/s providing a throughput efficiency of 59% of attainable FP64 peak on 99.9% of the machine. In addition, the team demonstrated low time step latency of 3.4 s/timestep on a protein fragment with 1,496 atoms and over 5,500 electrons attaining a simulation throughput of 25,000 time steps per day on 1,024 nodes of Perlmutter.

In their paper, the 2024 ACM Gordon Bell Prize-winning team claims, “This leap forward is not merely incremental; it redefines the boundaries of what is computationally feasible in molecular dynamics, setting a new benchmark for accuracy and efficiency in large-scale simulations. The enhanced scalability and accuracy of our simulation techniques empowers the scientific community to tackle longstanding challenges in both chemistry and biology.”

The Frontier supercomputer, located at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, is the world’s first and fastest exascale supercomputer. It can perform a quintillion (a billion billion) operations per second. When Frontier came online in 2022, it was 2.5 times faster than the world’s second most powerful supercomputer. As of November 2024, Frontier is ranked as the world’s second most powerful supercomputer. The Perlmutter supercomputer is housed at the (US) National Energy Research Scientific Computing Center (NERSC). It is used primarily in applications including climate analysis, quantum information science, clean energy technologies, as well as semiconductors and microelectronics. As of November 2024, it is ranked as the world’s 19^th most powerful supercomputer.

ACM today presented a 12-member team with the ACM Gordon Bell Prize for Climate Modelling for their project “Boosting Earth System Model Outputs and Saving PetaBytes in Their Storage Using Exascale Climate Emulators.” The award recognizes innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Sameh Abdulah, Marc G. Genton, David E. Keyes, Zubair Khalid, Hatem Ltaief, Yan Song, Greorgiy L. Stenchikov and Ying Sun (all of King Abdullah University of Science and Technology, Saudi Arabia); Allison H. Baker (NSF National Center for Atmospheric Research, USA); George Bosilca, (NVIDIA, USA); Qinglei Cao (St. Louis University, USA); and Stefano Castruccio (University of Notre Dame, USA).

Scientists have warned that global warming, caused by the human use of fossil fuels, is reaching a crisis point. Experts assert that the prevalence of more intense storms, hurricanes, droughts, wildfires, as well as a loss of biodiversity, are signs that the crisis is worsening rapidly. They warn that, if not urgently addressed, global warming poses an existential threat to life on Earth.

Using computational tools to better understand the rate and impacts of climate change is considered a valuable tool in developing strategies to address the problem. While climate modelling has been a scientific practice since the 1950’s, recently introduced exascale supercomputers (which can process a quintillion calculations each second) offer the opportunity to understand climate change at a far more advanced level than ever before. With the use of exascale computers, computer scientists and climate scientists have developed extremely high-resolution Earth System Models (ESM’s).

ESM’s offer great promise in understanding the Earth’s climate but they are computationally expensive—i.e., they require a great deal of computation time and energy, and they require a tremendous amount of storage for the massive quantity of data they generate.

To address this problem, the prize-winning team presented the design and implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. In computing, emulators allow for a dynamic interplay between different computers—with one computer system (called the host) behaving like another computer system (the guest). The growing use of emulators in climate modeling has become more common as emulators can combine and enhance efficient algorithms to handle large datasets, as well as the distribution of computations across multiple processors.

Climate emulators have come to play a pivotal role in alleviating the computational burden and storage requirements associated with climate modeling and simulations. Because the resolution of a climate model is impacted by the trade-off between the computational costs and the representation of the climate system, improving both computational and data storage challenges in a high-performance computer allows for more advanced climate modeling capabilities.

The ACM Gordon Bell Prize for Climate Modelling winning team contends their emulator could save several petabytes of computing storage space. By way of comparison, one petabyte is equal to the storage capacity of approximately 170 top-end servers.

The team’s ultra-high resolution model of the earth’s climate included 54,486,360 spatial locations around the globe, as well as 318 billion hourly and 31 billion daily observations.

The team achieved its results using high performance computing methods called Spherical Harmonic Transform (SHT) and Cholesky factorization. In the introduction to their paper, the Prize-winning team wrote: “We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in climate data. This provides tunable spatio-temporal resolution and significantly improves the fidelity and granularity of climate emulation, achieving an ultra-high spatial resolution of 0.034 ^◦ (∼3.5 km) in Space.”

The team ran mixed-precision computations on a PaRSEC dynamic runtime system, running on 9,025 nodes on Frontier, 1,936 nodes on Alps, 1,024 nodes on Leonardo, and 3,072 nodes on Summit, with the hybrid Flop/s rates 0.976 EFlop/s, 0.739 EFlop/s, 0.243 EFlop/s, and 0.375 EFlop/s, respectively.

The team concludes that their exascale climate emulator holds significant potential for the climate community, advancing climate research and policy making. They also maintain that their work holds significant potential in advancing the development of machine learning (ML) and AI-driven methods for forecasting or prediction applications in climate science.

New York, NY, September 18, 2024 – ACM, the Association for Computing Machinery, and IEEE Computer Society have named David A. Padua, Donald Biggar Willett Professor Emeritus in Engineering at the University of Illinois Urbana-Champaign, as the recipient of the 2024 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes groundbreaking achievements in parallel and high performance computing (HPC). Padua is cited for innovative and usable contributions to the theory and practice of parallel compilation and tools, as well as service to the computing community.

Parallel computing is a technique which involves taking a large computational task and breaking it up into smaller tasks so that it can be handled simultaneously on multiple processors. The overall goal is to reduce the amount of time required to process a task. While parallelization was initially employed as a tool in high performance computing, today it is used in almost every computing device including smartphones.

Padua has made fundamental contributions that have helped make parallel computation universally useful. He has worked at the levels of fundamental algorithms for parallelism, general tools for parallel programming (compilers and debuggers), and domain-specific languages, applications, and tools, as well as autotuning methods and compiler quality evaluation. His specific contributions include:

• Synchronization of multiple threads during computation is necessary for high performance computers to produce correct results. Two key techniques introduced by Padua and his students to enable the correct synchronization of multiple threads during parallel computing include speculative parallelism and array privatization.
• Padua and his students introduced much of the basic research that has made general parallel programming tools common today. He was directly involved in developing race detection for debugging parallel programs, as well as array tiling to improve parallelism and cache performance.
• In HPC, domain-specific parallelism has been especially effective in various special purpose applications. Padua’s contributions in this area include the development of parallel Matlab compilation, and contributions to compilation aspects of signal processing including the SPIRAL project.
• Padua and his students have made important contributions to the evaluation of compilers, including the vectorizing compiler evaluation techniques which have been used by compiler teams at Intel and other companies.

In addition to his technical contributions, Padua is recognized for outstanding service to the field. He integrated the ideas of parallel computing as editor of the Encyclopedia of Parallel Computing (Springer), a four-volume publication that is widely respected in the field. Padua has also served as editor of several esteemed parallel computing journals, chaired and/or participated in program committees for over 70 conferences, and supervised 37 PhD dissertations. His former students represent a new generation of talented individuals who have gone on to make significant contributions to both academia and industry.

The Ken Kennedy Award will be formally presented to Padua in November at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC24).

Biographical Background
David A. Padua is the Donald Biggar Willett Professor Emeritus of Engineering at the University of Illinois Urbana-Champaign. Padua received his Bachelor of Science degree in Computer Science from the Universidad Central de Venezuela, and a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

Padua is a Fellow of ACM, IEEE, and the American Association for the Advancement of Science (AAAS). His honors also include receiving the Harry H. Goode Memorial Award from the IEEE Computer Society, and an honorary PhD from the University of Vallodolid, Spain.

New York, NY, August 14, 2024 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that Ke Fan of the University of Illinois at Chicago and Daniel Nichols of the University of Maryland are the recipients of the 2024 ACM-IEEE CS George Michael Memorial HPC Fellowships. The George Michael Memorial Fellowship honors exceptional PhD students throughout the world whose research focus is high-performance computing (HPC) applications, networking, storage, or large-scale data analytics.

Fan is recognized for her research in three key areas of high-performance computing: optimizing the performance of MPI collectives, enhancing the performance of irregular parallel I/O operations, and improving the scalability of performance introspection frameworks. Nichols is recognized for advancements in machine-learning based performance modeling and the advancement of large language models for HPC and scientific codes.

Ke Fan

Fan’s research focuses on improving the performance of data movement associated with collective communication and parallel file I/O operations on large-scale supercomputers. Her most recent focus has been on two main areas: (1) Optimizing inter-process data movement, particularly in the context of all-to-all collectives, where all processes engage in data exchange. In this area, she has developed a new class of parameterized hierarchal algorithms that substantially improve the performance of both uniform and non-uniform all-to-all collectives. (2) Optimizing parallel I/O, targeting applications that generate unbalanced, irregular I/O workloads. Fan has specifically developed spatially aware data aggregation techniques that enhance load balancing and improve overall parallel I/O performance.

In addition to these two areas, she has made significant progress in improving the scalability of performance introspection frameworks, which help developers understand data movement capabilities in HPC systems. With these new insights, developers can identify bottlenecks and optimize performance at scale.

Daniel Nichols

Nichols’ research is broadly centered around the intersection of machine learning (ML) and high-performance computing. His most recent focus has been on two main areas: (1) developing novel ML-based performance models to make use of all available performance data when making predictions about code runtime properties, and (2) adapting state of the art large language model (LLM) techniques to HPC applications. By utilizing recent advances in representation learning and further advancing them to handle the unique challenges of performance modeling, Nichols’ research seeks to develop models that make use of all available data when predicting performance. This research has the potential to significantly improve both the quality and applicability of performance models.

By adapting LLM’s to HPC applications, Nichols’ work has improved their performance on HPC development tasks. He has created scientific and parallel code capable LLMs and methods for improving the quality of current models for HPC. This is part of his goal towards creating specialized LLMs to solve software complexities and allow scientists to focus on their domain research and less on the intricacies of HPC development.

The ACM-IEEE CS George Michael Memorial HPC Fellowship is endowed in memory of George Michael, one of the founders of the SC Conference series. The fellowship honors exceptional PhD students throughout the world whose research focus is on high performance computing applications, networking, storage, or large-scale data analytics using the most powerful computers that are currently available. The Fellowship includes a $5,000 honorarium and travel expenses to attend the SC conference, where the Fellowships are formally presented.

ACM and IEEE Computer Society named Wen-mei W. Hwu, a Senior Distinguished Research Scientist at NVIDIA and Professor Emeritus at the University of Illinois, Urbana-Champaign, the recipient of the ACM-IEEE CS Eckert-Mauchly Award. Hwu is recognized for pioneering and foundational contributions to the design and adoption of multiple generations of processor architectures. His fundamental and pioneering contributions have had a broad impact on three generations of processor architectures: superscalar, VLIW, and throughput-oriented manycore processors (GPUs).

Hwu was one of the original architects of the High-Performance Substrate (HPS) model that pioneered superscalar microarchitecture, introducing the concepts of dynamic scheduling, branch prediction, speculative execution, a post-decode cache, and in-order retirement. He co-authored the two original 1985 HPS papers, “Critical Issues Regarding HPS, a High Performance Microarchitecture” and “HPS, A New Microarchitecture: Rationale and Introduction,” both of which received the inaugural MICRO Test-of-Time Award in 2014.

By 1987, the rapid increase in hardware execution resources created pressing needs for instruction-level parallelizing compilers. Hwu addressed the problem by constructing a revolutionary compiler infrastructure in his paper, “IMPACT: An Architectural Framework for Multiple-Instruction Issue,” which demonstrated compilers can generate code with far more parallelism than most researchers thought possible. This paper also pioneered architecture support for control speculation and received the 2006 ISCA Most Influential Paper Award.

For his work on architecture support for ILP compilers, he received ACM SIGARCH’s first Maurice Wilkes award in 1998. He published foundational papers on superblock and hyperblock structures. The superblock is a pervasive compiler technique, adopted by major vendor compilers and the GNU C Compiler. In academia, the hyperblock work influenced many projects, most notably the TRIPS project at the University of Texas. In 1999, Hwu received the ACM Grace M. Hopper Award “for the design and implementation of the IMPACT compiler.”

Since 2006, Hwu has focused on designing and deploying throughput-oriented heterogeneous parallel computing architectures. His team pioneered the programmer optimization principles in their PPoPP 2008 paper and the Pareto-optimal pruning of search space for auto-tuning in their CGO 2008 paper for GPUs. The CGO 2008 paper won the 2018 CGO Test-of-Time Award  These works not only enabled wide adoption of CUDA-enabled GPUs but also helped the NVIDIA architecture team to improve the programmability of several generations of GPUs. The four editions of the textbook by Hwu and David Kirk (former Chief Scientist of NVIDIA), Programming Massively Parallel Processors, have sold more than 25,000 copies and the book has been translated into five languages.

Hwu’s contributions to education also include three offerings of the Coursera course on Heterogeneous Parallel Programming that were attended by more than 20,000 students, with 5,000 completing all exams and quizzes to receive a certificate. Hwu and Kirk are widely credited for their contributions in making the GPU the computing device of choice for the HPC/ML communities. Hwu’s architecture and compiler techniques have impacted billions of processors.

Hwu will be formally recognized with the Eckert-Mauchly Award during an awards luncheon on Tuesday, July 2, at the International Symposium on Computer Architecture (ISCA 2024 ).

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications,” which demonstrated wireless and batteryless sensor nodes using novel materials and radio backscatter.

Arora’s research envisions creating sustainable computational materials that operate by harvesting energy from the environment and, at the end of their life cycle, can be responsibly composted or recycled. Her research process involves working at the intersection of materials, methods of fabrication, low-power systems, and HCI . She actively looks to apply her work to application domains such as smart homes, health, climate change, and wildlife monitoring.

Arora’s dissertation makes truly groundbreaking contributions to the fields of Ubiquitous Computing and Human-Computer Interaction. Today’s Internet of Things (IoT) devices are bulky, require battery maintenance, and involve costly installation. In contrast, Arora shows how the computational capabilities of sensing, communication, and display can be diffused into materials and everyday objects. She builds interactive stickers that are inexpensive, and easy to deploy and sustainably operate by harvesting energy from body heat or indoor light. She demonstrates this idea over a series of projects. Her first effort, SATURN, is a thin, flexible multi-layer material that is a self-sustaining audio sensor. Specifically, it uses the vibration itself to power the ability to capture and encode the vibration sensor. SATURN was extended to ZEUSSS to use passive RF backscatter for wireless transmission on the vibration signal. She followed this up with the MARS platform that produces an extremely low-power (less than a microwatt) resonance circuit that varies its frequency based on user interaction with interfaces that create inductive or capacitive loads on the circuit. Coupling this circuit with FM passive backscatter and ambient power harvesting allows user interfaces such as touch-sensitive buttons, sliders, and vibration sensors to communicate at a distance. The result of these three projects is a flat user interface in a post-it note form factor that can be deployed in the environment simply by sticking it to a flat surface. The flat user interface and mobile design allows for applications such as light switches or audio volume sliders that can simply be pasted where they are needed without worrying about wiring the infrastructure or maintaining batteries.

The final project, VENUS, adds output in the form of low-power display technologies to provide immediate feedback on the surface of the computational material, opening a wide variety of user-facing interaction scenarios. Her work also showed that it is possible to power these circuits through the transfer of body heat when a user touches the button, which can also be used to protect privacy.

Arora is an Assistant Professor in the Electrical and Computer Engineering and (by courtesy) Computer Science Department, as well as the Allen K. and Johnnie Cordell Breed Jr. Professor of Design at Northwestern University. Her research involves rethinking the computing stack from a sustainability-first approach for its entire life-cycle: manufacturing, operation, and disposal. Arora received a PhD in Computer Science and an MS In Human-Computer Interaction from the Georgia Institute of Technology.

Honorable Mentions

Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Farina’s dissertation, “Game-Theoretic Decision Making in Imperfect-Information Games” was recognized for laying modern learning foundations for decision-making in imperfect-information sequential games, resolving long-standing questions, and demonstrating state-of-the-art theoretical and practical performance.

Farina is an Assistant Professor in the Electrical Engineering and Computer Science Department (EECS) at the Massachusetts Institute of Technology. His research interests include artificial intelligence, machine learning, optimization, and game theory. He received a PhD in Computer Science from Carnegie Mellon University.

Kuszmaul’s dissertation, “Randomized Data Structures: New Perspectives and Hidden Surprises,” is recognized for contributions to the field of randomized data structures that overturn conventional wisdom and widely believed conjecture.

Kuszmaul’s research focuses on algorithms, data structures, and probability. He received a PhD in Computer Science from the Massachusetts Institute of Technology and is presently doing Post Doctoral work at Harvard University. In August, he will be starting as an assistant professor in the Computer Science Department at Carnegie Mellon University.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Arora’s research envisions creating sustainable computational materials that operate by harvesting energy from the environment and, at the end of their life cycle, can be responsibly composted or recycled. Her research process involves working at the intersection of materials, methods of fabrication, low-power systems, and HCI . She actively looks to apply her work to application domains such as smart homes, health, climate change, and wildlife monitoring.

Arora is an Assistant Professor in the Electrical and Computer Engineering and (by courtesy) Computer Science Department, as well as the Allen K. and Johnnie Cordell Breed Jr. Professor of Design at Northwestern University. Her research involves rethinking the computing stack from a sustainability-first approach for its entire life-cycle: manufacturing, operation, and disposal. Arora received a PhD in Computer Science and an MS In Human-Computer Interaction from the Georgia Institute of Technology.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Kuszmaul’s dissertation, “Randomized Data Structures: New Perspectives and Hidden Surprises,” is recognized for contributions to the field of randomized data structures that overturn conventional wisdom and widely believed conjecture.

Kuszmaul’s research focuses on algorithms, data structures, and probability. He received a PhD in Computer Science from the Massachusetts Institute of Technology and is presently doing Post Doctoral work at Harvard University. In August, he will be starting as an assistant professor in the Computer Science Department at Carnegie Mellon University.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications. Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul of Harvard University.

Farina’s dissertation, “Game-Theoretic Decision Making in Imperfect-Information Games” was recognized for laying modern learning foundations for decision-making in imperfect-information sequential games, resolving long-standing questions, and demonstrating state-of-the-art theoretical and practical performance.

Farina is an Assistant Professor in the Electrical Engineering and Computer Science Department (EECS) at the Massachusetts Institute of Technology. His research interests include artificial intelligence, machine learning, optimization, and game theory. He received a PhD in Computer Science from Carnegie Mellon University.

Margaret Martonosi is the Hugh Trumbull Adams ’35 Professor of Computer Science at Princeton University. She also recently served a four-year term as the National Science Foundation (NSF) Assistant Director leading the Directorate for Computer and Information Science and Engineering (CISE).

Martonosi earned MS and PhD degrees in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Cornell University.

Her many honors include the ACM-IEEE-CS Eckert-Mauchly Award, the Undergraduate Research Mentoring Award by the National Center for Women & Information Technology, Princeton's Graduate Mentoring Award, as well as numerous Test-of-Time and Best Paper Awards. Martonosi is a Fellow of ACM and IEEE.  She is a member of the National Academy of Engineering.

Maja J. Matarić is the Chan Soon-Shiong Chair and Distinguished Professor of Computer Science at the University of Southern California, where she is the founding director of the USC Robotics and Autonomous Systems Center. She is also a Principal Scientist at Google DeepMind.

A graduate of the University of Kansas, Matarić earned an SM in Computer Science and a PhD in Computer Science and Artificial Intelligence from MIT. Her many publications cover a wide range of topics, including distributed robotics, robot learning, human-robot interaction, and socially assistive robotics, and are highly cited (h-index 105; over 45600 citations) .

Matarić is a member of the American Academy of Arts and Sciences (AMACAD), and Fellow of the American Association for the Advancement of Science (AAAS), the Association for the Advancement of Artificial Intelligence (AAAI), IEEE and ACM, and recipient of the US Presidential Award for Excellence in Science, Math, and Engineering Mentoring.

ACM and the Computer Science Teachers Association (CSTA) selected four high school students from among a pool of graduating high school seniors throughout the US for the ACM/CSTA Cutler-Bell Prize in High School Computing. Eligible students applied for the award by submitting a project/artifact that engages modern technology and computer science. A panel of judges selected the recipients based on the ingenuity, complexity, relevancy, and originality of their projects.

The Cutler-Bell Prize promotes the field of computer science and empowers students to pursue computing challenges beyond the traditional classroom environment. In 2015, David Cutler and Gordon Bell established the award. Cutler is a software engineer, designer, and developer of several operating systems at Digital Equipment Corporation. Bell, an electrical engineer, is Researcher Emeritus at Microsoft Research.

Each Cutler-Bell Prize winner receives a $10,000 cash prize. The prize amount is sent to the financial aid office of the institution the student will be attending next year and is then put toward each student’s tuition or disbursed. This year’s Cutler-Bell Prize recipients will be formally recognized at the Computer Science Teachers Association’s 2024 Annual Conference, July 16-19, in Las Vegas.

The winning projects illustrate the diverse applications the next generation of computer scientists is developing.

Shobhit Agarwal, Reedy High School, Frisco, Texas

Every three years, Shobhit Agarwal visits his grandparents in Jhansi, India, a town with the poorest healthcare infrastructure in northern India—where 85% of the population does not attend yearly checkups. After seeing a mental decline in his grandfather, Agarwal immersed himself in his grandparents’ hometown, interviewing local citizens and doctors. He was driven to create a low-cost system that recognizes a variety of diseases while simultaneously forecasting the progress of the disease. This system is OmniDoc, a framework that accurately predicts diagnoses, prognoses, and treatments given a patient profile. This system is currently deployed in Jhansi in two ways. First, hospital volunteers at the Naja Hospital conducted door-to-door visits to collect patient information. This data is inputted into Agarwal’s framework, and if the algorithm detects a disease, a patient books a free appointment with a local physician; the model data is subsequently forwarded to the doctor. Five hundred homes were visited, and nearly 40% of those patients were sent to clinics based on algorithm-identified risk. Hospital statistics show that 95% of the patients who arrived in the clinic due to the campaign had the algorithm’s diagnosis subsequently confirmed by a physician, indicating that the model accurately identified patient condition with minimal data. This success led to the deployment of OmniDoc at the Jhansi Orthopedic Hospital for their 15-person radiology department.

Franziska Borneff, Hidden Valley High School, Cave Spring, Virginia

During her junior year, Franziska Borneff became interested in monitoring the flow rates of Arctic rivers. Using her newly developed coding skills, she began to plot and trend analysis for climate research. Her own research aligned with the news articles about climate change, and this influenced her to continue to study these data trends. The Arctic rivers are important in the process of detecting climate change, as they directly influence ecosystems and the livelihoods of humans. Borneff researched the relationship between atmosphere and waterways, concluding that air temperature, river discharge, and sea ice concentration are the most significant data points. Her research creates a method to track critical dates related to the spring thaw of Arctic rivers and assess their impact on local populations. She collected daily temperature, river discharge, and sea ice cover data from publicly available sources for the six major Arctic rivers, finding the thaw date is earlier than ever. Borneff then connected with the students of the Yupik Eskimo Village in Marshall, Alaska, to see how the earlier thaw impacted the local community. Through this meeting, she read stories about how the familiar rhythms of nature have been disrupted and the uncertain future of the Yupik people as a result of these shifts. Borneff hopes her research will spark studies in sensitive Arctic regions and enlighten politicians to initiate government action that is informed by scientific understanding. The collaboration between quantitative data documenting warming and human narratives testifying to the accuracy of the data is essential.

Daniel Mathew, Poolesville High School, Poolesville, Maryland

Daniel Mathew’s project, MiniMesh, danced between invention and improvement. Starting out on his bedside desk, Mathew dumped out scrap electronic parts and assembled a device named PreVis to track the location of a single point on a human for movement analysis with a LiDAR mounted on a controllable servo. PreVis went everywhere, from discussing him in Congress to presenting him at research conferences. PreVis was Mathew’s first step toward human-computer interaction. PreVis then evolved into MiniProse, a free mobile application. Mathew studied the mathematics of current pose estimation solutions and then developed the core algorithm for MiniProse. The final algorithm, MiniMesh, scrapped the original two prototypes and was built from the ground up. Mathew developed a new framework. Mathematically proving optimization identities and writing machine learning algorithms, MiniMesh could reconstruct the entire human mesh with thousands of points. Mathew took this algorithm to companies for skin cancer research and military organizations for augmented-reality surgery. By predicting the location of thousands of points on the human body on portable devices, all in real-time, MiniMesh has applications in several fields. In the original biomechanical use case, MiniMesh could be used for at-home gait analysis by tracking joint locations and improving sports techniques by analyzing the metabolic cost of human movements. For assistive surgery, MiniMesh accurately and efficiently maps the human topology to aid surgeons. For animation, MiniMesh replicates expensive VFX, enabling amateur animators at no cost.

Kosha Upadhyay, Bellevue High School, Bellevue, Washington

Kosha Upadhyay watched her neighbor of eight years struggle with dementia. Following his passing, she wanted to understand the struggle of people with dementia and began volunteering at a dementia care center. Upadhyay knew she couldn’t give these patients back what they lost but wanted to preserve what they had left. This inspired her to work on creating a better therapy for those suffering from dementia. This therapy, MemSpark, is an automated end-to-end system that creates a novel brain-training therapy using virtual reality and tracks dementia progression through artificial intelligence. Upadhyay studied brain morphology and neurodegeneration to create a therapy that affected dementia at its root cause. Since decline can affect any part of the brain, she designed a novel set of games that exercised all parts of the brain. Eight serious games were designed to exercise all cognitive functions by considering a set of promotive factors (immersion, confidence, focus) and preventive factors (anxiety, frustration, self-pity). Each serious game produced a set of two features—accuracy and time which were then inputted into an AI model for profiling. The data generated by the Virtual Reality (VR) system was pre-processed and passed into an AI model. After studying multiple AI models, Upadhyay chose a multi-layer perceptron neural network due to its suitability for the mode of data, along with its high accuracy and regression adaptability. The neural network produced cognitive profiling scores across three categories: recall, reasoning, and executive function. The profiles were aligned to the ADAS-Cog test–an industry standard test for evaluating dementia. Upadhyay tested her therapy across 14 people split into experimental and control groups. Her solution was able to slow down dementia progression by 65%—which surpasses current mainstream therapies and presents the potential to enhance the way we approach dementia care.

Amanda Randles is the Alfred Winborne and Victoria Stover Mordecai Associate Professor of Biomedical Sciences at Duke University. A graduate of Duke University with a BA in Physics and Computer Science, Randles received a PhD degree in Applied Physics and an SM degree in Computer Science from Harvard University.

Randles’s honors include the ACM SIGHPC Emerging Woman Leader in Technical Computing Award, the NIH Pioneer Award, the NSF CAREER Award, the ACM Grace Murray Hopper Award, and the Alexandra Jane Noble Epiphany Award. Randles is a National Academy of Inventors Fellow, an ACM Distinguished Member, an IEEE Senior Member, and was named as an MIT TR35 Visionary.

Avi Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. He has been a leading figure in areas including computational complexity theory, algorithms and optimization, randomness and cryptography, parallel and distributed computation, combinatorics, and graph theory, as well as connections between theoretical computer science and mathematics and science.

Wigderson’s honors include the Abel Prize, the IMU Abacus Medal (previously known as the Nevanlinna Prize), the Donald E. Knuth Prize, the Edsger W. Dijkstra Prize in Distributed Computing, and the Gödel Prize. He is an ACM Fellow and a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences.

ACM, the Association for Computing Machinery, has named 68 Fellows for transformative contributions to computing science and technology. All the 2023 inductees are longstanding ACM Members who were selected by their peers for groundbreaking innovations that have improved how we live, work, and play.

“The announcement each year that a new class of ACM Fellows has been selected is met with great excitement,” said ACM President Yannis Ioannidis. “ACM is proud to include nearly 110,000 computing professionals in our ranks and ACM Fellows represent just 1% of our entire global membership. This year’s inductees include the inventor of the World Wide Web,the “godfathers of AI, and other colleagues whose contributions have all been important building blocks in forming the digital society that shapes our modern world.

In keeping with ACM’s global reach, the 2023 Fellows represent universities, corporations, and research centers in Canada, China, Germany, India, Israel, Norway, Singapore, the United Kingdom, and the United States. The contributions of the 2023 Fellows run the gamut of the computing field―including algorithm design, computer graphics, cybersecurity, energy-efficient computing, mobile computing, software analytics, and web search, to name a few.

Additional information about the 2023 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows website.

Read the news release.

ACM has named 52 Distinguished Members for significant contributions. All the 2023 inductees are longstanding ACM Members and were selected by their peers for work that has advanced computing, fostered innovation across various fields, and improved computer science education.

“The ACM Distinguished Members program recognizes both career achievement as well as participation in ACM,” said ACM President Yannis Ioannidis. “Many of these new 52 Distinguished Members have been selected for important technical achievements, while others have been chosen because of their service and/or work in computer science education, which lays the foundation for the future of our field. With the Distinguished Member designation, ACM also highlights how individual computing professionals maintain the health and growth of a global scientific society through membership and active engagement with their colleagues.” v vThe 2023 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Belgium, Canada, China, Denmark, Finland, France, Germany, India, Israel, Italy, Switzerland, the United Kingdom, and the United States. This year’s class of Distinguished Members made advancements in areas including AI and economics, principles of data management, software development, human-computer interaction, developing technology for people with disabilities, mobile and wireless sensing systems, and many others.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience and significant achievements in the computing field. To be nominated, a candidate must have at least 15 years of professional experience in the computing field, five years of professional ACM membership in the last 10 years, and must have achieved a significant level of accomplishment or made a significant impact in the field of computing. A Distinguished Member is expected to have served as a mentor and role model by guiding technical career development and contributing to the field beyond the norm.

Read the ACM news release.

The ACM India 2023 Doctoral Dissertation Award goes to Pranjal Dutta for his dissertation titled “A Tale of Hardness, De-randomization and De-bordering in Complexity Theory.” Pranjal’s dissertation makes breakthrough contributions in the study of hardness, approximation and derandomization of algebraic circuits, and develops new mathematical tools, laying the foundation for future developments in algebraic complexity theory. Pranjal’s dissertation work was done at Chennai Mathematical Institute under the supervision of Prof. Nitin Saxena.

Pranjal Dutta’s dissertation makes multiple contributions to algebraic complexity theory. In an early result, he shows that the existence of a polynomial with a slightly largish special representation would resolve the long-standing VP vs VNP problem, and also provide non-trivial algorithms for polynomial identity testing (PIT) – effectively attacking two flagship problems in algebraic complexity theory. In the context of PIT, his dissertation provides a nearly polynomial time algorithm for derandomization of a special class of depth 4 algebraic circuits, where the top fanin is bounded and the bottom gates compute low degree polynomials. This is among the strongest results in PIT known so far. Pranjal also provides a deep understanding of the power of closure of algebraic circuits -- an important problem in the Geometric Complexity Theory approach to the P vs NP problem. Specifically, he shows that the closure (or border) of (bounded fanin) depth 3 circuits can be captured by algebraic branching programs, making it one of the first such "de-bordering" results in this area. Additionally, his work also establishes an efficient algorithm for PIT for this class, and shows a surprising exponential-gap hierarchy-theorem for depth-3 constant-top-fanin circuits.

The Honorable Mention for 2023 goes to Jogendra Nath Kundu for his dissertation titled “Self-supervised Domain Adaptation Framework for Computer Vision Tasks.” Jogendra’s dissertation makes significant and timely contributions to unsupervised domain adaptation and self-supervised learning techniques for structured prediction based vision tasks, advancing the practical deployment of intelligent machines in real-world scenarios. His dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Prof. Venkatesh Babu Radhakrishnan.

Jogendra Nath Kundu’s doctoral dissertation makes three important contributions. First, he considers image-like dense-prediction tasks, where he addresses shortcomings in existing classification-based domain adaptation algorithms. The content-preserving mechanisms introduced by him via cyclic consistency objectives have been shown to yield state-of-the-art performance. Next, he introduces source-side procurement stage learning, a novel approach to source-free adaptation, which significantly enhances adaptability in scenarios with restricted data-sharing. Finally, Jogendra’s thesis applies domain adaptation concepts to the complex task of 3D human pose estimation. He shows how this can be achieved by incorporation of effective prior-enforcing mechanisms alongside development of novel self-supervised techniques leveraging inter-entity relations. The insights in Jogendra’s dissertation have the potential to revolutionize the deployment of intelligent vision systems across diverse industries, from healthcare to virtual and augmented reality.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation in Computer Science and related disciplines from a degree-awarding institution based in India for each academic year, running from July 1 of one year to June 30 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award, given to nomination(s), if any, that missed the award by a narrow margin, is accompanied by a prize of ₹1,00,000, that is shared among the recipient(s). The winning dissertation(s) will be published in the ACM Digital Library. Tata Consultancy Services Limited (TCS)  is the founding sponsor of these awards. Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

ACM, the Association for Computing Machinery, named an eight-member team drawn from American and Indian institutions as the winner of the 2023 ACM Gordon Bell Prizefor the project, “Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys.”

The members of the team are: Sambit Das (University of Michigan), Bikash Kanungo (University of Michigan), Vishal Subramanian, (University of Michigan), Gourab Panigrahi (Indian Institute of Science, Bangalore), Phani Motamarri (Indian Institute of Science, Bangalore), David Rogers (Oakridge National Laboratory), Paul M. Zimmerman (University of Michigan), and Vikram Gavini (University of Michigan).

Molecular dynamics is a process by which computer simulations are used to better understand the movements of atoms and molecules within a system. Ab initio (Latin for “from the beginning”) is a branch of molecular dynamics that has been shown to be an especially effective technique when applied to important problems in physics and chemistry—including efforts to better understand microscopic mechanisms, gain new insights in materials science, and prove out experimental data.

Despite the successes of ab initio approaches in a wide range of computer simulations, the team notes that efforts to employ quantum mechanical ab initio methods to predict materials’ properties has not been able to achieve quantum accuracy and scale on the powerful supercomputers needed to perform these simulations. In their abstract to their Gordon Bell Prize-winning project the authors write, “ Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum Many-Body (QMB) methods realize quantum accuracy but fail to scale.”

To address this challenge, the Gordon Bell Prize-winning team developed a framework that combines the accuracy provided by QMB methods with the efficiency of Density-Functional Theory (DFT) to access larger length scales at quantum accuracy—a goal that existing approaches have not been able to achieve.

The 2023 ACM Gordon Bell Prize-winning team writes, “We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.”

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC23), which was held in Denver, Colorado.

ACM, the Association for Computing Machinery, presented a nineteen-member team with the inaugural  ACM Gordon Bell Prize for Climate Modelling for their project, “The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System.” The new award aims to recognize innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Mark A. Taylor, Luca Bertagna, Conrad Clevenger, James G. Foucar, Oksana Guba, Benjamin R. Hillman, Andrew G. Salinger (all of Sandia National Laboratories); Peter M. Caldwell, Aaron S. Donahue, Noel Keen, Christopher R. Terai, Renata B. McCoy, David C. Bader (all of Lawrence Livermore National Laboratory); Jayesh Krishna, Danqing Wu (both of Argonne National Laboratory); Matthew R. Norman, Sarat Sreepathi (both of Oakridge National Laboratory); James B. White III (Hewlett Packard Enterprise); and L. Ruby Leung (Pacific Northwest National Laboratory).

To develop the most effective carbon emission reduction policies, governments are working with scientists to better understand the relationship between carbon emissions, the earth’s atmosphere, and climate change. Because of the mind-boggling number of variables in understanding climate phenomena (e.g., temperature, humidity, precipitation), scientists have increasingly used powerful supercomputers to process all these variables in order to develop high resolution simulations.

Climate scientists are especially interested in understanding convective clouds (clouds that are formed by the process of warmer air rising above a less dense atmosphere). Deep convective clouds (which can be many kilometers thick) are particularly important to simulate correctly because they drive the tropical overturning circulation and modulate energy transfer over much of the planet.

A class of algorithmic models known as global cloud-resolving models (GCRMs) have been used to attempt to simulate deep convective clouds and have been accurate in certain instances such as providing simulations of short time periods or limited physical areas. But the Prize-winning team notes that the drawbacks of GCRM’s include the fact that running these algorithms on existing supercomputers has been slow and computationally expensive (e.g., the algorithms require too many steps).

The team proves that by using just-introduced exascale supercomputers along with a new algorithmic model they have introduced, the longstanding challenge of developing efficient and accurate simulations of deep convective clouds can be accomplished. The prize-winning team introduces the new algorithmic model, “Simple Cloud Resolving E3SM Atmosphere Model (SCREAM).“

The ACM Gordon Bell Prize for Climate Modelling aims to recognize innovative parallel computing contributions toward solving the global climate crisis. Climate scientists and software engineers are evaluated for the award based on the performance and innovation in their computational methods.

New York, NY, October 26, 2023 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that James Gregory Pauloski of the University of Chicago and Rohan Basu Roy of Northeastern University are the recipients of the 2023 ACM-IEEE CS George Michael Memorial HPC Fellowships. Hua Huang of the Georgia Institute of Technology received an Honorable Mention. Pauloski is recognized for developing systems for optimal HPC resource usage from scalable optimization methods for deep learning training to data fabrics for sophisticated applications spanning heterogeneous resources. Roy is recognized for enhancing the productivity of computational scientists and environmental sustainability of HPC with novel methods and tools exploiting cloud computing and on-premise HPC resources. Huang is recognized for contributions to high performance parallel matrix algorithms and implementations and their application to quantum chemistry calculations.

J. Gregory Pauloski

Pauloski’s aim is to build tools that are approachable and easily used by HPC novices and experts alike.

His research approaches HPC from two aspects: efficient large scale machine learning (ML) training, and data fabrics that support distributed and federated scientific applications. The rapid increase in demand for AI tools (e.g., ChatGPT, LaMDA, etc.) has promoted scalable deep learning to a core challenge for HPC. Pauloski has made advancements in system software and algorithms to efficiently use novel hardware systems for AI applications. He has also worked on the development of federated applications which span heterogeneous systems composed of specialized accelerators, edge devices, cloud compute, and HPC. Pauloski’s work on data fabrics enables autonomous actors to communicate efficiently and reliably, independent of location.

In addition to his technical contributions, Pauloski’s colleagues have cited his work as a role model and mentor for younger students.

Rohan Basu Roy

Roy designs new tools and methods for enhancing HPC programmer productivity and making large-scale computing systems more cost-effective and environmentally sustainable.

The key challenges computational scientists face include the time required to performance-tune their code and the time required to efficiently provision and utilize computing resources. To address these challenges, Roy has designed HPC performance auto-tuner tools to significantly improve the program productivity. Roy’s research contributions include the first demonstration of significant productivity and performance advantages of the serverless computing model for complex scientific workflows, including quick elasticity in the cloud (eliminating the long queue wait time), ease of use, and opportunistic co-location for better resource utilization.

Opportunistic co-location of workloads in the cloud reduces the carbon footprint of on-premise HPC cluster/supercomputer -- Roy continues to aim toward improving the environmental sustainability of HPC systems as their carbon footprint is increasing rapidly.

Hua Huang

Huang has focused on developing new algorithms and implementations for high-performance matrix computations. His research problems have mostly come from the area of quantum chemistry and electronic structure calculations. Huang’s innovations have been integrated into widely used codes such as Psi4, NWChem, and SPARC, as well as a proxy application, GTFock, from Georgia Tech.

His many contributions include developing a high-performance, multi-purpose, rank-structured matrix library for multiple scientific computing tasks, designing innovative parallel algorithms for large-scale matrix operations, etc. He also introduced new optimization strategies for constructing the Fock and density matrices in quantum chemistry calculations.

About the ACM IEEE CS George Michael Memorial Fellowship

The ACM-IEEE CS George Michael Memorial HPC Fellowshipis endowed in memory of George Michael, one of the founders of the SC Conference series. The fellowship honors exceptional PhD students throughout the world whose research focus is on high performance computing applications, networking, storage, or large-scale data analytics using the most powerful computers that are currently available. The Fellowship includes a $5,000 honorarium and travel expenses to attend the SC conference, where the Fellowships are formally presented.

New York, NY, October 4, 2023 – ACM, the Association for Computing Machinery, and IEEE Computer Society have named Keshav Pingali, the W.A. ”Tex” Moncrief Chair of Grid and Distributed Computing at the University of Texas at Austin, as the recipient of the 2023 ACM-IEEE CS Ken Kennedy Award. The Ken Kennedy Award recognizes groundbreaking achievements in parallel and high-performance computing. Pingali is cited for contributions to high-performance parallel computing for irregular algorithms such as graph algorithms. He is also cited for leadership on the Galois Project, which provides a unifying framework for parallelizing both irregular and regular algorithms.

Pingali has made deep and wide-ranging contributions to many areas of parallel computing including programming languages, compilers, and runtime systems for multicore, manycore and distributed computers. These include program transformation algorithms for cache optimization, representations for program restructuring, and symbolic analysis techniques for complex numerical algorithms. These contributions have been incorporated into most open-source and commercial compilers.

Pingali’s most recent research has focused on foundational parallel programming abstractions and implementations for irregular algorithms, which use complex data structures like sparse matrices and graphs. Traditional techniques for exploiting parallelism in regular dense matrix algorithms fail when applied to irregular algorithms. The Ken Kennedy award recognizes Pingali’s “operator formulation of algorithms,” which is a programming and execution model that is remarkably simple yet powerful enough to capture patterns of parallelism in both regular and irregular algorithms. The Galois system implements this model, and it is used in diverse areas including real-time intrusion detection in computer networks, parallel tools for asynchronous circuit design, and machine learning on graphs for drug discovery. In addition, Pingali is being recognized for his distinguished mentoring of computer science leaders and students during his career.

The award will be formally presented to Pingali in November at The International Conference for High-Performance Computing, Networking, Storage and Analysis (SC23).

Biographical Background
Keshav Pingali is the William “Tex” Moncrief Chair of Grid and Distributed Computing at the University of Texas at Austin. Before moving to UT Austin, Pingali was the India Chair of Computing in the Department of Computer Science at Cornell University. Pingali received the Bachelor of Technology (BTech) degree from the Indian Institute of Technology, Kanpur, where he was awarded the President’s Gold Medal; the Master of Science (S.M.) and Electrical Engineering (E.E.) degrees from the Massachusetts Institute of Technology; and a Doctor of Science (ScD) degree from the Massachusetts Institute of Technology.

He is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE), a Foreign Member of the Academia Europaea, and a Distinguished Alumnus of the Indian Institute of Technology, Kanpur. In March 2023, he was awarded the IEEE Charles Babbage award. In 1998, the College of Arts & Sciences at Cornell University awarded him the Stephen & Margery Russell Distinguished Teaching for "superlative performance in the classroom.” He has served on the NSF CISE Advisory Committee (2009-2012), and he was co-Editor-in-Chief of the ACM Transactions on Programming Languages and Systems (2007-2010).

ACM and IEEE Computer Society named Kunle Olukotun, a Professor at Stanford University, as the recipient of the ACM-IEEE CS Eckert-Mauchly Award for contributions and leadership in the development of parallel systems, especially multicore and multithreaded processors.

In the early 1990s, Olukotun became a leading designer of a new kind of microprocessor known as a "chip multiprocessor"—today called a "multicore processor." His work demonstrated the performance advantages of multicore processors over the existing microprocessor designs at the time. He included these ideas in a landmark paper presented at the ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 1996), entitled "The Case for a Single-Chip Multiprocessor." This paper received the ASPLOS Most Influential Paper Award 15 years later. Olukotun’s multicore design eventually became the industry standard.

His insights on multicore processors and thread-level speculation research laid the foundation for Olukotun's work on fine-grained multithreading, a technique which improves the overall efficiency of computer processors (CPUs). These designs were the basis for Afara WebSystems, a server company Olukotun founded that was eventually acquired by Sun (and later Oracle). Sun Microsystems used Olukotun’s designs as a foundation for its Niagara chips, which were recognized for their outstanding performance and energy efficiency. The Niagara family of chips are now used in all of Oracle's SPARC-based servers.

Later, with Christos Kozyrakis and others, Olukotun was a leader in designing the Transactional Coherence and Consistency (TCC) approach to simplify parallel programming. He was a co-author of the paper, “Transactional Memory Coherence and Consistency,” which was presented at the 2004 International Symposium on Computer Architecture (ISCA) and received the Most Influential Paper Award in 2019. Olukotun is one of only two researchers who have received the Most Influential Paper Award from both ASPLOS and ISCA.

ACM and IEEE Computer Society co-sponsor the Eckert-Mauchly Award, which was initiated in 1979. It recognizes contributions to computer and digital systems architecture and comes with a $5,000 prize. The award was named for John Presper Eckert and John William Mauchly, who collaborated on the design and construction of the Electronic Numerical Integrator and Computer (ENIAC), the pioneering large-scale electronic computing machine, which was completed in 1947.

He will be formally recognized with the Eckert-Mauchly Award during an awards luncheon on Tuesday, June 20th at the International Symposium on Computer Architecture (ISCA 2023).

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions,” which established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.

The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

The mathematical object that Jain’s thesis constructs, indistinguishability obfuscation, is considered a theoretical “master tool” in the context of cryptography—not only in helping achieve long-desired cryptographic goals such as functional encryption, but also in expanding the scope of the field of cryptography itself. For example, indistinguishability obfuscation aids in goals related to software security that were previously entirely in the domain of software engineering.

Jain’s dissertation was awarded the Best Paper Award at the ACM Symposium on Theory of Computing (STOC 2021) and was the subject of an article in Quanta Magazine titled “Scientists Achieve Crown Jewel of Cryptography.”

Jain is an Assistant Professor at Carnegie Mellon University. He is interested in theoretical and applied cryptography and its connections with related areas of theoretical computer science. Jain received a BTech in Electrical Engineering, and an MTech in Information and Communication Technology from the Indian Institute of Technology, Delhi. He received a PhD in Computer Science from the University of California, Los Angeles.

Honorable Mentions

Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Suhr’s dissertation, “Reasoning and Learning in Interactive Natural Language Systems,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Suhr is an Assistant Professor at the University of California, Berkeley. Suhr’s research is focused on natural language processing, machine learning, and computer vision. Suhr received a BS in Computer Science and Engineering from Ohio State University, as well as a PhD in Computer Science from Cornell University.

Watt’s dissertation, “Mechanising and Evolving the Formal Semantics of WebAssembly: the Web’s New Low-Level Language,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

Watt is a Research Fellow (postdoctoral) at the University of Cambridge, where he focuses on mechanized formal verification, concurrency, and the WebAssembly language. He received a MEng in Computer Science from Imperial College London and a PhD in Computer Science from the University of Cambridge.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Jain's dissertation established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

Jain’s dissertation was awarded the Best Paper Award at the ACM Symposium on Theory of Computing (STOC 2021) and was the subject of an article in Quanta Magazine titled “Scientists Achieve Crown Jewel of Cryptography.”

Jain is an Assistant Professor at Carnegie Mellon University. He is interested in theoretical and applied cryptography and its connections with related areas of theoretical computer science. Jain received a BTech in Electrical Engineering, and an MTech in Information and Communication Technology from the Indian Institute of Technology, Delhi. He received a PhD in Computer Science from the University of California, Los Angeles.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Suhr’s dissertation, “Reasoning and Learning in Interactive Natural Language Systems,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Suhr is an Assistant Professor at the University of California, Berkeley. Suhr’s research is focused on natural language processing, machine learning, and computer vision. Suhr received a BS in Computer Science and Engineering from Ohio State University, as well as a PhD in Computer Science from Cornell University.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “Indistinguishability Obfuscation From Well-Studied Assumptions.” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt, who earned his PhD at the University of Cambridge.

Watt’s dissertation, “Mechanising and Evolving the Formal Semantics of WebAssembly: The Web’s New Low-Level Language,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

Watt is a Research Fellow (postdoctoral) at the University of Cambridge, where he focuses on mechanized formal verification, concurrency, and the WebAssembly language. He received a MEng in Computer Science from Imperial College London and a PhD in Computer Science from the University of Cambridge.

ACM and the Computer Science Teachers Association (CSTA) selected four high school students from among a pool of graduating high school seniors throughout the US for the ACM/CSTA Cutler-Bell Prize in High School Computing. Eligible students applied for the award by submitting a project/artifact that engages modern technology and computer science. A panel of judges selected the recipients based on the ingenuity, complexity, relevancy, and originality of their projects.

The Cutler-Bell Prize promotes the field of computer science and empowers students to pursue computing challenges beyond the traditional classroom environment. In 2015, David Cutler and Gordon Bell established the award. Cutler is a software engineer, designer, and developer of several operating systems at Digital Equipment Corporation. Bell, an electrical engineer, is researcher emeritus at Microsoft Research.

Each Cutler-Bell Prize recipient receives a $10,000 cash prize. The prize amount is sent to the financial aid office of the institution the student will be attending next year and is then put toward each student’s tuition or disbursed.

These projects illustrate the diverse applications being developed by the next generation of computer scientists.

Okezue Bell, Moravian Academy, Bethlehem, Pennsylvania

With only 1% of the computer science space being Black, the innovation landscape within the field is reflective of that. There are few race- and class-responsible solutions built effectively for communities that have been historically discriminated against. “Fidutam” is a novel, putative first effort to foster a needs-responsive approach to providing financial accounts for unbanked populations. Bell uses state-of-the-art encryption to ensure the safety of user’s data, creating private signatures using a selfie, name and personal data, and location. The solution focuses on financial documentation, which is proven to be the biggest barrier to entry for the unbanked, Bell created Fidutam not only to provide financial access to unbanked individuals but to develop a platform to enable the upward mobilization of the global poor to revive their community’s economy. This increases their share in the global development landscape of computer science and also encourages and enables those whose voices are often underrepresented in CS to penetrate or quality control the field to ensure the existing products have utility in their milieus.

Nathan Elias, Liberal Arts and Sciences Academy, Austin, Texas

In his project, “A Novel Method for Automated Identification and Prediction of Invasive Species Using Deep Learning,” Elias developed InvasiveAI, a service that helps farmers, agricultural workers, and average citizens in the fight against invasive species. He designed an app that utilizes Artificial Intelligence and machine learning methods to accurately detect, predict, and visualize invasive species growth. Using the app, 200 unique invasive plants, wildlife, insects, and pathogens, can be identified. Elias also created a 3D image detection algorithm to identify over 75 invasive species aerially. Elias envisions that InvasiveAI will contribute to the field of computer science by expanding CS’ reach in environmental and citizen science systems, while also furthering advancements in geospatial and AI-based tracking toolkits. This project was inspired by the loss of Elias’ grandfather’s farm in Southern India to the invasive species Kariba.

Hannah Guan, BASIS San Antonio Shavano, San Antonio, Texas

Guan’s project, “Multi-Dimensional Interpretable Interaction Network (MDiiN) for Modeling Aging Heath and Mortality” was inspired by the retired military population of the city of San Antonio. She wanted to create an efficient and affordable system that would be able to diagnose and find remedies for highly pervasive age-related diseases like cancer or Alzheimer’s. Guan’s research can influence elders’ quality and equity of life worldwide. MDiiN is a computation and affordable predictive model that evaluates health risk factors for elders. It’s the first three-dimensional interaction network to uncover high-dimensional interactions among health variables during the aging process. Doctors can use MDiiN to predict the onset of age-related diseases, which would significantly increase the quality and longevity of life across the grid. It’s fast and easy to run, taking less than a second to get results. This research contributes to computer science by strengthening health equality in our society, improving global health security, and leading to tremendous public health benefits.

Sirihaasa Nallamothu, University High School, Normal, Illinois

In her project, Predicting and Identifying Relevant Features of Vasovagal Syncope in Patients with Postural Orthostatic Tachycardia Syndrome (POTS) using machine learning methods and physiological data, was inspired from a TikTok that led Nallamothu down a rabbit hole about POTS. To her surprise, there were no research studies or consumer solutions to predict syncope on real-world data, and she was determined to use her machine learning skills to predict syncopal episodes. Nallamothu is the first person to conduct an IRB research study and collect human subject field data on POTS patients in the real world using non-invasive technologies. She wrote a Python script to extract the 15-minute window signal data of heart rate, blood volumetric pressure, EDA, temperature, and accelerometer data. Nallamothu also uses the concept called “late fusion” in temporal multimodal machine learning. This research is providing a starting point for future research into real-time prediction and integration into a smartwatch, which will help millions who experience vasovagal syncope research a safe and comfortable position before fainting. After completing her research, Nallamothu plans to work toward creating a consumer product and pairing her algorithm with a smart watch.-->{C}

Margo Seltzer is the Canada 150 Research Chair and the Cheriton Family Chair in Computer Science at the University of British Columbia. She is also the Director of the Berkman Center for Internet and Society at Harvard University.

Seltzer earned a PhD degree in Computer Science from the University of California at Berkeley, and an AB degree in Applied Mathematics from Harvard/Radcliffe College. She has authored more than 194 publications on a wide range of topics related to computer systems including systems for capturing and accessing data provenance, file systems, databases, and storage.

Her honors include receiving the UBC CS Awesome Instructor Award, the ACM SIGMOD Systems Award, the USENIX Lifetime Achievement Award, the CRA-E Undergraduate Research Mentoring Award, and the ACM Software System Award (for BerkeleyDB), among many others. She is a Fellow of ACM, the American Academy of Arts and Sciences, and the National Academy of Engineering.

David B. Papworth was employed at Intel Corporation from 1990 to 2020, having served in positions including Principal Processor Architect, and Intel Fellow. He has broad experience in CPU microarchitecture, the software/hardware interface, and is listed as co-inventor on more than 50 issued patents for his work.

Papworth received a Bachelor of Science in Electrical Engineering from the University of Michigan, Ann Arbor. His honors include receiving the Intel Achievement Award for Microarchitecture, and the Intel Achievement Award for producing a microprocessor chip in record time.

Yael Tauman Kalai is a Senior Principal Researcher at Microsoft Research and an Adjunct Professor at the Massachusetts Institute of Technology (MIT). Kalai earned a BSc in Mathematics from the Hebrew University of Jerusalem, an MS in Computer Science and Applied Mathematics from The Weizmann Institute of Science, and a PhD in Computer Science from the Massachusetts Institute of Technology.

Kalai’s honors include the George M. Sprowls Award for Best Doctoral Thesis in Computer Science (MIT, 2007), an IBM PhD Fellowship (2004-2006), an MIT Presidential Graduate Fellowship (2003-2006), and an Outstanding Master’s Thesis Prize (Weizmann Institute of Science, 2001). She is a Fellow of the International Association for Cryptologic Research (IACR). Additionally, Kalai gave an Invited Talk at the International Congress of Mathematics (ICM, 2018).

Siddharth Sandipkumar Bhandari is the recipient of the ACM India 2022 Doctoral Dissertation Award for his dissertation titled “Exact Sampling and List Decoding” that develops new techniques and tools in sampling graph colourings and contributes to improved analyses for understanding list-decodability of codes. Siddharth’s doctoral dissertation work was done at Tata Institute of Fundamental Research, Mumbai under the supervision of Prahladh Harsha.

Siddharth’s dissertation makes fundamental contributions to two different areas of theoretical computer science: (a) sampling colourings of graphs and (b) list-decoding error-correcting codes. Sampling a random k-colouring of a given graph is a classic problem in theoretical computer science and statistical physics. The problem of perfect sampling is to construct a polynomial time randomised algorithm that generates a perfect sample from the uniform distribution of k-colourings. Siddharth’s work improves on a two-decade old algorithm for solving this problem using novel techniques that are likely to have wider applicability. In the area of list-decoding, Siddharth’s dissertation focuses on three problems. The first problem is the zero-error capacity of the q/(q 1) channel. Prior work on perfect hashing shows that as the list-size is reduced, the channel capacity decreases from an inverse-polynomial form to an inverse-exponential form, and it has been an open problem to understand where this transition occurs. By showing that the channel follows a coupon-collector like behaviour, Siddharth’s work demonstrates that there is an almost sharp transition near O(q log q). The other two results in this part are related to list-decodability of algebraic codes and lead to a better understanding of the list-decoding of these codes all the way up to capacity.

Pritish Mohapatra shares the Honorable Mention for his dissertation titled “Optimization for and by Machine Learning” that makes significant contributions towards the design of efficient optimization methods for machine learning, including key results for optimizing ranking metrics used in information retrieval systems. Pritish’s doctoral dissertation work was done at International Institute of Information Technology, Hyderabad under the supervision of C. V. Jawahar.

Pritish’s dissertation addresses the problem of making optimization of complicated loss functions efficient and practical. Specifically, he designs a practically efficient optimization algorithm for a category of non-decomposable ranking metrics that greatly improve the feasibility of using such sophisticated metrics for learning in information retrieval tasks. Pritish also provides a concrete theoretical analysis that proves the superiority of the proposed algorithm for optimization, providing a fine balance between the theoretical soundness and practical utility of the algorithms. Pritish’s work also makes an interesting contribution in the use of the classical optimization technique of partial-linearization for efficient learning of large-scale classification models. This holds special significance for learning of models with structured output spaces. Finally, Pritish’s dissertation contributes to the problem of using learning for optimization. He proposes a novel framework that uses a learnable model for doing rounding for combinatorial optimization algorithms. This is based on a key insight that randomized rounding procedures can be visualized as sampling from latent variable models.

Sruthi Sekar shares the Honorable Mention for her dissertation titled “Near-Optimal Non-Malleable Codes and Leakage Resilient Secret Sharing Schemes” that makes fundamental contributions in our understanding of cryptographic primitives such as non-malleable codes, secret sharing schemes and randomness extractors. Sruthi’s doctoral dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Bhavana Kanukurthi.

The goal of non-malleable codes (NMCs) is to enable encoding of messages so that an adversary cannot tamper it into the encoding of a related message. An open problem in this area is to build 1/2-rate NMCs in 2-split state model where a codeword consists of two independently tamperable states. Sruthi’s dissertation makes fundamental contributions to this area. Specifically, she builds on her earlier work to introduce new two-state non-malleable codes for random messages with rate 1/2. This work also introduces a new primitive called "Non-malleable randomness encoders". Her dissertation also shows how to build a near-optimal rate 1/3, 2-state NMC, thereby taking us a step closer to solving the main open problem in this area. In addition, Sruthi’s dissertation introduces a new pseudorandomness primitive called "Adaptive Extractors" and shows their applicability to building constant-rate leakage resilient secret sharing schemes.

Deepika Yadav shares the Honorable Mention for her dissertation titled “Supporting Ongoing Training of Community Health Workers through Mobile-based Solutions in Rural India” that combines the knowledge of Computer System research and HCI to produce systems that are deployed in fields. Her work resulted in deployment of a mobile based training platform for ASHA workers that has been used to train hundreds of ASHA workers. Deepika’s doctoral dissertation work was done at Indraprastha Institute of Information Technology Delhi under the supervision of Pushpendra Singh.

Deepika’s dissertation develops a low-cost mobile-based training platform called “Sangosthi” that allows a geographically distributed group of community health workers (CHWs) to connect over a conference call and receive training in a structured manner. The developed system uses a hybrid architecture to use Interactive Voice Response for facilitating online audio training sessions, enabling CHWs to access training from anywhere through their feature phones. Her work contributes to (i) testing the feasibility and efficacy of a low-cost technology intervention through a controlled field experiment (ii) unpacking the training needs of CHWs in the field and mapping it back to the existing reference material through a large-scale deployment on 500 CHWs, (iii) investigating the potential for peer-to-peer learning models to address the challenge of experts’ availability through a controlled field experiment, and (iv) exploring the potential for automated techniques in this domain by proposing a semi-automated natural language processing approach for curating generated learning content and exposing CHWs and women to Chatbot-based education for the first time. By using a range of mixed methods and field experiments, Deepika’s dissertation expands the focus of HCI4D and mHealth research on CHW competence development in low-resource settings.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation from a degree-awarding institution based in India for each academic year, running from August 1 of one year to July 31 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award is accompanied by a prize of ₹1,00,000 which is shared amongst the recipients. The dissertation(s) will be published in the ACM Digital Library. Financial support for both the award and honorable mention is provided by Tata Consultancy Services (TCS). Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

ACM, the Association for Computing Machinery, has named 57 of its members ACM Fellows for wide-ranging and fundamental contributions in disciplines including cybersecurity, human-computer interaction, mobile computing, and recommender systems among many other areas. The accomplishments of the 2022 ACM Fellows make possible the computing technologies we use every day.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

“Computing’s most important advances are often the result of a collection of many individual contributions, which build upon and complement each other,” explained ACM President Yannis Ioannidis. “But each individual contribution is an essential link in the chain. The ACM Fellows program is a way to recognize the women and men whose hard work and creativity happens inconspicuously but drives our field. In selecting a new class of ACM Fellows each year, we also hope that learning about these leaders might inspire our wider membership with insights for their own work.”

In keeping with ACM’s global reach, the 2022 Fellows represent universities, corporations, and research centers in Canada, Chile, China, France, Germany, Israel, the Netherlands, Spain, Switzerland, and the United States.

Additional information about the 2022 ACM Fellows, as well as previously named ACM Fellows, is available through the ACM Fellows website.

Read the news release.

ACM has named 67 Distinguished Members for significant contributions. All of the 2022 inductees are longstanding ACM members and were selected by their peers for work that has spurred innovation, enhanced computer science education, and moved the field forward.

“The ACM Distinguished Members program honors both accomplishment and commitment,” said ACM President Yannis Ioannidis. “Each of these new 67 Distinguished Members have been selected for specific and impactful work, as well as their longstanding commitment to being a part of our professional association. As ACM celebrates its 75th anniversary this year, it is especially fitting to reflect on how our global membership has built our organization into what it is today. Our Distinguished Members are leaders both within ACM and throughout the computing field.”

The 2022 ACM Distinguished Members work at leading universities, corporations and research institutions in Australia, Canada, China, Finland, France, Germany, Greece, India, Italy, Japan, the Netherlands, New Zealand, Singapore, Taiwan, the United Kingdom, and the United States. ACM Distinguished Members are selected for their contributions in three separate categories: educational, engineering, and scientific. This year’s class of Distinguished Members made advancements in areas including algorithms, computer science education, cybersecurity, data management, energy efficient computer architecture, information retrieval, healthcare information technology, knowledge graph and semantic analysis, mobile computing, and software engineering, among many others.

The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience and significant achievements in the computing field. To be nominated, a candidate must have at least 15 years of professional experience in the computing field, five years of professional ACM membership in the last 10 years, and must have achieved a significant level of accomplishment or made a significant impact in the field of computing. A Distinguished Member is expected to have served as a mentor and role model by guiding technical career development and contributing to the field beyond the norm.

Read the ACM news release.

ACM, the Association for Computing Machinery named a 16-member team drawn from French, Japanese, and US institutions as recipient of the 2022 ACM Gordon Bell Prize for their project, “Pushing the Frontier in the Design of Laser-Based Electron Accelerators With Groundbreaking Mesh-Refined Particle-In-Cell Simulations on Exascale-Class Supercomputers.”

The members of the team are: Luca Fedeli, France Boillod-Cerneaux, Thomas Clark, Neil Zaїm, and Henri Vincenti, (CEA); Axel Huebl, Kevin Gott, Remi Lehe, Andrew Myers, Weiqun Zhang, and Jean-Luc Vay, (Lawrence Berkeley National Laboratory); Conrad Hillairet, (Arm); Stephan Jaure, (ATOS); Adrien Leblanc, (Laboratoire d’Optique Appliquée, ENSTA Paris); Christelle Piechurski, (GENCI); and Mitsuhisa Sato, (RIKEN).

Particle-in-Cell (PIC) simulation is a technique within high-performance computing used to model the motion of charged particles, or plasma. PIC has applications in many areas, including nuclear fusion, accelerators, space physics, and astrophysics. The very recent introduction of exascale-class computers has expanded the horizons of PIC simulations and makes this year’s winning project especially exciting. According to their abstract, the team presents a first-of-kind mesh-refined (MR) massively parallel PIC code for kinetic plasma simulations optimized on the Frontier, Fugaku, Summit, and Perlmutter supercomputers.

The 2022 ACM Gordon Bell Prize-winning team concludes by noting that, “the use of mesh refinement in large-scale electromagnetic PIC simulations is a first and represents a paradigm shift. The successful modeling with savings between 1.5× and 4× with mesh refinement that is reported in this paper is a landmark steppingstone toward a new era in the modelling of laser-plasma interactions.”

The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics. The award was presented during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), which was held in Dallas, Texas.