About ACM Gordon Bell Prize

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.

ACM, the Association for Computing Machinery, named a 14-member team, drawn from Chinese institutions, recipients of the 2021 ACM Gordon Bell Prize for their project, Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer.

The members of the winning team are: Yong (Alexander) Liu, Xin (Lucy) Liu, Fang (Nancy) Li, Yuling Yang, Jiawei Song, Pengpeng Zhao, Zhen Wang, Dajia Peng, and Huarong Chen of Zhejiang Lab, Hangzhou and the National Supercomputing Center in Wuxi; Haohuan Fu and Dexun Chen of Tsinghua University, Beijing, and the National Supercomputing Center in Wuxi; Wenzhao Wu of the National Supercomputing Center in Wuxi; and Heliang Huang and Chu Guo of the Shanghai Research Center for Quantum Sciences.

Quantum supremacy is a term used to denote the point at which a quantum device can solve a problem that no classical computer can solve in a reasonable amount of time. Teams at Google and the University of Science and Technology of China in Hefei both claim to have developed devices that have achieved quantum supremacy.

According to the Gordon Bell Prize recipients, determining whether a device has achieved quantum supremacy for a given task (in a specific scenario) begins with sampling the interactions of the different quantum bits (qubits) in a random quantum circuit (RQC). As the number of possible interactions among qubits in a random quantum circuit is staggeringly large, simulating their interactions is a problem well-suited for a high-performance computer. However, the quantum physics behind the entangled qubits requires that the classical binary bits used in a supercomputer store and compute the information with exponentially-increasing complexity.

In their Gordon Bell Prize-winning work, the Chinese researchers introduced a systematic design process that covers the algorithm, parallelization, and architecture required for the simulation. Using a new Sunway Supercomputer, the Chinese team effectively simulated a 10x10x (1+40+1) random quantum circuit (a new milestone for classical simulation of RQC). Their simulation achieved a performance of 1.2 Eflops (one quintillion floating-point operations per second) single-precision, or 4.4 Eflops mixed-precision, using over 41.9 million Sunway cores (processors).

The project far outpaced state-of-the-art approaches to simulating an RQC. For example, the most recent effort, using the Summit supercomputer to simulate a random quantum circuit of the Google Sycamore quantum processor (which has 53 qubits), was estimated to take 10,000 years to perform. By contrast, the Chinese team’s approach employing the Sunway supercomputer takes only 304 seconds for a simulation of similar quantum complexity.

The Chinese team explained that they undertook this challenge because achieving real-time simulation of an RQC using a supercomputer would aid both in the development of quantum devices and in bringing algorithmic and architectural innovations within the traditional supercomputing community.

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 today by former ACM President Cherri M. Pancake and Professor Mark Parsons, Chair of the 2021 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21), which was held in St. Louis, Missouri, and virtually for those who could not attend.

ACM, the Association for Computing Machinery, named a nine-member team, drawn from Chinese and American institutions, recipients of the 2020 ACM Gordon Bell Prize for their project, “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning.”

Winning team members include Weile Jia, University of California, Berkeley; Han Wang, Institute of Applied Physics and Computational Mathematics (Beijing, China); Mohan Chen, Peking University; Denghui Lu, Peking University; Lin Lin, University of California, Berkeley and Lawrence Berkeley National Laboratory; Roberto Car, Princeton University; Weinan E, Princeton University; and Linfeng Zhang, Princeton University.

The famed physicist Richard Feynman once said, “If we were to name the most powerful assumption of all, which leads one on and on to an attempt to understand life, it is that all things are made of atoms, and that everything that living things do can be understood in terms of the jiggling and wiggling of atoms.” Molecular dynamics (MD) is a computer simulation method that analyzes how atoms and molecules move and interact during a fixed period of time. MD simulations allow scientists to gain a better sense of how a system (which could include anything from a single cell to a cloud of gas) progresses over time. Practical applications of molecular dynamics include studying large molecules such as proteins for drug development.

Ab initio (meaning in Latin “from the beginning” or “from first principles”) Molecular Dynamics (AIMD) is an approach that differs slightly from Standard Molecular Dynamics (SMD) in how interatomic forces are calculated during the simulation. The level of precision that can be gained through AIMD has made it the preferred simulation method of scientists for more than 35 years. At the same time, while AIMD allows for greater accuracy, the approach requires more computation—and has therefore been limited to the study of small-sized systems (systems that have a maximum size of thousands of atoms).

In their Gordon Bell Prize-winning paper, the team introduced Deep Potential Molecular Dynamics (DPMD). DPMD is a new machine learning-based protocol that can simulate a more than 1 nanosecond-long trajectory of over 100 million atoms per day. While other machine learning-based protocols have been introduced for MD simulations in recent years, the authors contend that their protocol achieves the first efficient MD simulation of 100 million atoms with ab initio accuracy.

As the Gordon Bell Prize recognizes achievement in high performance computing, finalists must demonstrate that their proposed algorithm can scale (run efficiently) on the world’s most powerful supercomputers. The team developed a highly optimized code (GPU Deep MD-Kit), which they successfully ran on the Summit supercomputer. The team’s GPU Deep MD-Kit efficiently scaled up to the entire Summit supercomputer, attaining 91 PFLOPS (1 PFLOP = 1 quadrillion floating operation points per second) in double precision (45.5% of the peak) and 162/275 PFLOPS in mixed-single/half precision.

The Summit supercomputer, developed by IBM for the (US) Oak Ridge National Laboratory, was the first supercomputer to reach exaflop speed (1 quintillion operations per second), and was the world’s fastest supercomputer from November 2018 to June 2020.

In the abstract of their paper, the Gordon Bell Prize winning team wrote, “The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.”

The award was presented by ACM President Gabriele Kotsis and Bronis de Supinski, Chair of the 2020 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC20), which was held virtually for the first time.

ACM named a six-member team from the Swiss Federal Institute of Technology (ETH) Zurich recipients of the 2019 ACM Gordon Bell Prize for their project, “A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations.”

The ETH Zurich team introduced DaCe OMEN, a new framework for simulating the transport of electrical signals through nanoscale materials (such as the silicon atoms used in transistors). To better understand the thermal properties of transistors, the team simulated how electricity would be transported through a two-dimensional slice of transistor consisting of 10,0000 atoms. The ETH Zurich researchers simulated the 10,000-atom system 14 times faster than an earlier framework that was used for a 1,000-atom system. The DaCe OMEN code they developed for the simulation has been run on two top-6 hybrid supercomputers, reaching a sustained performance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) in double precision, and 90.89 Pflop/s in mixed precision.

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 by ACM President Cherri M. Pancake and Arndt Bode, Chair of the 2019 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) in Denver, Colorado.

Today’s commercial microchips contain 100,000,000 transistors in the span of a single millimeter, and managing heat generation and dissipation is one of the central problems in computer architecture. As the transistors on each microchip have become smaller and more densely packed, the amount of heat they generate has steadily increased. The cooling systems needed to keep supercomputers and data centers from overheating have become increasingly expensive. They estimate that cooling can consume up to 40% of the total electricity needed for data centers, amounting to cumulative costs of many billions of dollars per year.

Today’s supercomputers, which can perform up to 200 quadrillion calculations per second, allow scientists in many disciplines to gain new insights by processing a staggering number of variables. The ETH Zurich team used their simulation to develop a map of where heat is produced in a single transistor, how it is generated and how it is evacuated. It is hoped that a deeper understanding of these thermal characteristics could inform the development of new semiconductors with optimal heat-evacuating properties.

In recent years, the OMEN framework has been a popular quantum transport simulator for modeling nanoscale materials, but has experienced scaling bottlenecks. The ETH Zurich Team wrote a variation of OMEN that is Data Centric (DaCe OMEN). “We show that the key to eliminating the scaling bottleneck is in formulating a communication-avoiding algorithm,” the team writes in their paper. The ETH Zurich team’s solver yields data movement characteristics that can be used for performance and communication modeling, communication avoidance, and dataflow transformations. They go on to note that the speedup made by the DaCe OMEN framework is two orders of magnitude faster per atom than the original OMEN code.

The ETH Zurich team also built a graphical interface for the DaCe OMEN framework that includes a visualization of dataflow in lieu of a simple textual description. Anyone running the code can use the image representation to interact with the data directly. The team believes this new innovation could be applied to numerous scientific disciplines beyond nanoelectronics.

Winning team members include Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider and Torsten Hoefler, from ETH Zurich’s Scalable Parallel Computing Laboratory, as well as Guillermo Indalecio Fernández and Mathieu Luisier from ETH Zurich’s Integrated Systems Laboratory.

ACM named two teams to receive the 2018 ACM Gordon Bell Prize. A seven-member team affiliated with the Oak Ridge National Laboratory is recognized for their paper “Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction,” and a 12-member team affiliated with the Lawrence Berkeley National Laboratory is recognized for their paper “Exascale Deep Learning for Climate Analytics.”

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 by ACM President Cherri M. Pancake and Valerie Taylor, Chair of the SC18 Awards Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC18) in Dallas, Texas. Prior to the awards ceremony, all of the Gordon Bell Prize finalists presented their papers during SC18.

Employing Supercomputers to Combat the Opioid Epidemic
Paper Title: “Attacking the Opioid Epidemic: Determining the Epistatic and Pleiotropic Genetic Architectures for Chronic Pain and Opioid Addiction"
Prize Category: Sustained Performance Prize
Team: Oak Ridge National Laboratory
According to the US Centers for Disease Control and Prevention (CDC), 115 people die every day in the US from opioid overdoes. Additionally, the CDC found that there was a 30% increase in opioid overdoes in the period between July 2016 and September 2017 in 52 areas and 45 states. The aim of the Oak Ridge National Laboratory (ORNL) team is to use supercomputing to provide a tool in combating the opioid epidemic by understanding the underlying genetic architecture of how individuals develop chronic pain and respond to opioids. ORNL team members also believe that their project will help with the identification of new therapeutic approaches for opioid misuse. Genome-wide association studies (GWASs) have led to important discoveries in varied types of diseases. For a genome dataset, the ORNL team had access to the Million Veterans Program (MVP), a joint initiative of the US Department of Energy and the US Veterans Administration (VA). The MVP dataset includes 750,000 human genome types, associated with more than a billion medical records over a 20-year period.

The ORNL team developed a new “CoMet” algorithm that allows supercomputers to process vast amounts of genetic data and identify genes that may be more susceptible to pain and opioid addiction—as well as promising treatments. By running the ORNL team’s algorithm, supercomputers were able to successfully process genetic data at a magnitude that is four to five times greater than the latest state-of-the-art approaches. In addition to processing information about the genetics of pain and opioid addiction, CoMet is currently being used in projects ranging from bioenergy to clinical genomics.

The ORNL team includes Daniel Jacobson, Wayne Joubert, Deborah Weighill, and David Kainer (all of Oak Ridge National Laboratory); Sharlee Climer (University of Missouri-St. Louis); Amy Justice (Yale University/Department of Veterans Affairs); and Kjiersten Fagnan (US Department of Energy Joint Genome Institute).

Employing Deep Learning Methods to Understand Weather Patterns
Paper Title: “Exascale Deep Learning for Climate Analytics"
Prize Category: Scalability and Time to Solution
Team: Lawrence Berkeley National Laboratory
Climate change poses a major challenge to humanity in the 21st century. Increasingly, state and local governments are interested in the question of how extreme weather events will change (or affect) their local communities. In order to address these important questions, climate scientists routinely configure and run high-fidelity simulations under a range of different climate change scenarios. Recently, it has been shown that deep learning methods, wherein artificial neural networks vaguely inspired by the human brain learn from large amounts of data, can be applied to better understand extreme weather conditions. Using high-performance computers, the Lawrence Berkeley National Laboratory (LBNL) team trained a deep neural network to identify extreme weather patterns from high-resolution climate simulations. They demonstrated that accurate datasets can be computed for weather patterns such as tropical cyclones and atmospheric rivers.

To train the neural network, the LBNL team paper proposed an innovative blend of hardware and software solutions. These included a novel architecture as well as a number of system-level innovations to enable the largest graphics processing units (GPU)-based HPC systems in the world to process vast amounts of weather-related data. Their application represents the largest successful high performance computer scaling of a deep learning application to date.

Winning team members from the LBNL include Mr Prabhat, Thorsten Kurth, Mayur Mudigonda, Jack Deslippe, Ankur Mahesh (all from Lawrence Berkeley National Laboratory); Sean Treichler, Joshua Romero, Nathan Luehr, Everett Phillips, Massimiliano Fatica, Michael Houston (all of NVIDIA); and Michael Matheson (Oak Ridge Leadership Computing Facility).

Innovations from advanced scientific computing have a far-reaching impact in many areas of science and society—from understanding the evolution of the universe and other challenges in astronomy, to complex geological phenomena, to nuclear energy research, to economic forecasting, to developing new pharmaceuticals. The annual SC conference brings together scientists, engineers and researchers from around the world for an outstanding week of technical papers, timely research posters, and tutorials.

ACM named a 12-member Chinese team the recipients of the 2017 ACM Gordon Bell Prize for their research project, “18.9-Pflops Nonlinear Earthquake Simulation on Sunway TaihuLight: Enabling Depiction of 18-Hz and 8-Meter Scenarios.” Using the Sunway TaihuLight, which is ranked as the world’s fastest supercomputer, the team developed software that was able to efficiently process 18.9 Pflops (or 18.9 quadrillion calculations per second) of data and create 3D visualizations relating to a devastating earthquake that occurred in Tangshan, China in 1976. The team’s software included innovations that achieved greater efficiency than had been previously attained running similar programs on the Titan and TaihuLight supercomputers.

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 today by ACM President Vicki Hanson and Subhash Saini, Chair of the 2017 Gordon Bell Prize Award Committee, during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC17) in Denver, Colorado.

Although earthquake prediction and simulation is an inexact and emerging area of research, scientists hope that the use of supercomputers, which can process vast sets of data to address the myriad of variables at play in geologic events, may lead to better prediction and preparedness. For example, the Chinese team’s 3D simulations may inform engineering standards for buildings being developed in zones known to have seismic activity. In this vein, many have advocated for a significant increase in the amount of sensors to regularly monitor seismic activity. The Tangshan earthquake, which occurred on July 28, 1976 in Tangshan, Hebei, China, is regarded as the most devastating earthquake of the 20^th century, and resulted in approximately 242,000-700,000 deaths. In developing their simulations for the Tangshan earthquake, the winning team included input data from the entire spatial area of the quake, a surface diameter of 320 km by 312 km, as well as 40 km deep below the earth’s surface. The input data also included a frequency range of the earthquake of up to 18 Hz (Hertz). In the study of earthquakes, a Hertz is a unit of measurement that measures the number of times an event happens in the period of a second. For example, it might correspond to the number of times the ground shakes back and forth during an earthquake. Previous simulations of violent earthquakes have employed a lower frequency than 18 Hz, since enormous memory and time consumption are needed for high frequency simulations.

This year’s winning team is not the first to develop algorithms for supercomputers in an effort to simulate earthquake activity. In the abstract of their presentation, the 2017 Gordon Bell recipients write: “Our innovations include: (1) a customized parallelization scheme that employs the 10 million cores efficiently at both the process and thread levels; (2) an elaborate memory scheme that integrates on-chip halo exchange through register communication, optimized blocking configuration guided by an analytic model, and coalesced DMA access with array fusion; (3) on-the-fly compression that doubles the maximum problem size and further improves the performance by 24%."

Of its new innovations, the Chinese team adds that its on-the-fly compression scheme may be effectively applied to other challenges in exascale computing. In their paper, the authors state: “The even more exciting innovation is the on-the-fly compression scheme, which, at the cost of an acceptable level of accuracy lost, scales our simulation performance and capabilities even beyond the machine’s physical constraints. While the current compression scheme is largely customized for our specific application and the Sunway architecture, we believe the idea has great potential to be applied to other applications and other architectures.”

Winning team members include Haohuan Fu, Tsinghua University and National Supercomputing Center, Wuxi, China; Conghui He, Tsinghua University and National Supercomputing Center, Wuxi, China; Bingwei Chen, Tsinghua University and National Supercomputing Center, Wuxi, China; Zekun Yin, Shandong University; Zhenguo Zhang, Southern University of Science and Technology, China; Wenqiang Zhang, University of Science and Technology of China; Tingjian Zhang, Shandong University; Wei Xue, Tsinghua University and National Supercomputing Center, Wuxi, China; Weiguo Liu, Shandong University; Wanwang Yin, National Research Center of Parallel Computer Engineering and Technology, China; Guangwen Yang, Tsinghua University and National Supercomputing Center, Wuxi, China; and Xioafei Chen, Southern University of Science and Technology, China.

Innovations from advanced scientific computing have a far-reaching impact in many areas of science and society—from understanding the evolution of the universe and other challenges in astronomy, to complex geological phenomena, to nuclear energy research, to economic forecasting, to developing new pharmaceuticals. The annual SC conference brings together scientists, engineers and researchers from around the world for an outstanding week of technical papers, timely research posters, and tutorials.

The Sunway TaihuLight is a Chinese supercomputer with over 10.5 M heterogeneous cores and is ranked as the fastest supercomputer in the world.  Located at the National Supercomputer Center in Wuxi, Jingsu, China, it is nearly three times as fast as the Tianhe-2, the supercomputer that previously held the world record for speed.

ACM named a 12-member Chinese team the recipients of the 2016 ACM Gordon Bell Prize for their research project, “10M-Core Scalable Fully-Implicit Solver for Nonhydrostatic Atmospheric Dynamics.” The winning team presented a solver (method for calculating) atmospheric dynamics. 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 bestowed during the International Conference for High Performance Computing, Networking, Storage and Analysis (SC16) in Salt Lake City, Utah.

Since the dawn of computing, scientists have used data analytics in an effort to predict and simulate the weather and related atmospheric events. In the early years of weather forecasting, scientists might have used standard central processing units (CPUs). With each passing year, the continued expansion in the capabilities of high performance computers has enabled researchers to employ increasingly sophisticated computational methods for the analysis and modeling of weather patterns. Advanced scientific computers break problems down into composite parts and perform immense amounts of mathematical calculations simultaneously. The performance of a supercomputer is measured in floating-point operations per second (FLOPS). Some of the latest supercomputers are capable of performing quadrillions of FLOPS.

In the abstract of their presentation, the winning team writes, “On the road to the seamless weather-climate prediction, a major obstacle is the difficulty of dealing with various spatial and temporal scales. The atmosphere contains time-dependent multi-scale dynamics that support a variety of wave motions.”

To simulate the vast number of variables inherent in a weather system developing in the atmosphere, the winning group presents a highly scalable fully implicit solver for three-dimensional nonhydrostatic atmospheric simulations governed by fully compressible Euler equations. Euler equations are a set of equations frequently used to understand fluid dynamics (liquids and gasses in motion).

Elaborating further, they add, “In the solver, we propose a highly efficient domain-decomposed multigrid preconditioner that can greatly accelerate the convergence rate at the extreme scale. For solving the overlapped subdomain problems, a geometry-based pipelined incomplete LU factorization method is designed to further exploit the on-chip fine-grained concurrency.”

The fully-implicit solver successfully scales to the entire system of the Sunway TaihuLight, a Chinese supercomputer with over 10.5 M heterogeneous cores, allowing for a performance of 7.95 PFLOPS in double precision. The Chinese team contends that this is the largest fully-implicit simulation to date. The Sunway TaihuLight is ranked as the fastest supercomputer in the world. It is nearly three times as fast as the Tianhe-2, the supercomputer that previously held the world record for speed.

Winning team members include Chao Yang, Chinese Academy of Sciences; Wei Xue, Tsinghua University; Haohuan Fu, Tsinghua University; Hongtao You, National Research Center of Parallel Computer Engineering and Technology; Xinliang Wang, Beijing Normal University; Yulong Ao, Chinese Academy of Sciences; Fangfang Liu, Chinese Academy of Sciences; Lin Gan, Tsinghua University; Ping Xu, Tsinghua University; Lanning Wang, Beijing Normal University; Guangwen Yang, Tsinghua University; and Weimin Zheng, Tsinghua University.

Innovations from advanced scientific computing have a far-reaching impact in many areas of science and society—from understanding the evolution of the universe and other challenges in astronomy, to complex geological phenomena, to nuclear energy research, to economic forecasting, to developing new pharmaceuticals. The annual SC conference brings together scientists, engineers and researchers from around the world for an outstanding week of technical papers, timely research posters, and tutorials.

 Austin, Texas, November 20, 2015 – A 10-member team led by Johann Rudi of the University of Texas at Austin are the recipients of the 2015 ACM Gordon Bell Prize for their entry entitled An Extreme-Scale Implicit Solver for Complex PDEs: Highly Heterogeneous Flow in Earth's Mantle. The winning team includes representatives from the University of Texas at Austin, IBM Corporation, California Institute of Technology and the Courant Institute of Mathematical Sciences at New York University. 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 bestowed during SC15 (sc15.supercomputing.org) in Austin, Texas.

Denver, Colorado, November 22, 2013 – Scientists from Switzerland, Germany and the U.S have set a new supercomputing simulation record in fluid dynamics by reaching 14.4 Petaflops of sustained performance to win the 2013 ACM Gordon Bell Prize awards.acm.org/bell.

The simulation, which represents a 150- fold improvement over current state-of-the-art performance levels for this type of application, has potential utility for improving the design of high pressure fuel injectors and propellers, shattering kidney stones, and therapeutic approaches for cancer treatment. The research was conducted by scientists at ETH Zurich and IBM Research, in collaboration with the Technical University of Munich and the Lawrence Livermore National Laboratory (LLNL). The results were presented by the team at SC13 sc13.supercomputing.org in Denver, where the recipient of the ACM Gordon Bell Prize was announced on November 21. The simulation conducted by the team resolved unique phenomena associated with clouds of collapsing bubbles. This condition occurs when vapor bubbles formed in a liquid collapse due to changes in pressure. The successful effort employed 13 trillion cells and 6.4 million threads on LLNL’s “Sequoia” IBM BlueGene/Q, one of the fastest supercomputers in the world. The simulation resolved 15,000 bubbles and a 20-fold reduction in time to solution over previous research. The paper describing this achievement was one of six papers chosen as finalists for the 2013 Gordon Bell Prize awarded by ACM acm.org.

Members of the team included Diego Rossinelli, Babak Hejazialhosseini, Panagiotis Hadjidoukas, and Petros Koumoutsakos from ETH Zurich; Costas Bekas and Alessandro Curioni from IBM Zurich Research Laboratory; Adam Bertsch and Scott Futral from Lawrence Livermore National Laboratory; and Steffen Schmidt and Nikolaus Adams from Technical University Munich.

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