Separate teams running on Oak Ridge National Laboratory’s Summit are taking home the two Gordon Bell Prize awards this year

A team from the University of California Berkeley, Peking University, Lawrence Berkeley National Laboratory, and Princeton University won the 2020 ACM Gordon Bell Prize. Image Credit: SC20

Today at the 2020 International Conference for High Performance Computing, Networking, Storage and Analysis, or SC20, virtual conference, two deserving teams were awarded two distinguished prizes for research conducted on the Oak Ridge Leadership Computing Facility’s (OLCF’s) Summit, the nation’s fastest supercomputer.

Co-lead researcher Linfeng Zhang; Image Credit: Princeton University

The Association for Computing Machinery (ACM) Gordon Bell Prize, which recognizes outstanding achievement in high-performance computing (HPC), was presented to a team of researchers from Lawrence Berkeley National Laboratory’s Computational Research Division; the University of California, Berkeley; the Institute of Applied Physics and Computational Mathematics, Peking University; and Princeton University for its successful testing of the DeePMD-kit software package, named for “deep potential molecular dynamics.”

The team refers to DeePMD-kit as a “HPC+AI+Physical model” in that it combines high-performance computing (HPC), artificial intelligence (AI), and physical principles to achieve both speed and accuracy for molecular dynamics (MD) simulations.

Simulating a block of copper atoms, the team put DeePMD-kit to the test on Summit with the goal of seeing how far they could push the simulation’s size and timescales beyond AIMD’s accepted limitations. They were able to simulate a system of 127.4 million atoms—more than 100 times larger than the current state of the art. Furthermore, the simulation achieved a time-to-solution mark of at least 1,000 times faster at 2.5 nanoseconds per day for mixed-half precision, with a peak performance of 275 petaflops (one thousand million million floating-point operations per second) for mixed-half precision.

Co-lead researcher Weile Jia; Image Credit: UC Berkeley

“By combining physical principles and the representation power of deep neural networks, the Deep Potential method can achieve very good accuracy, especially for complex problems,” said Weile Jia, a postdoc in applied mathematics in Professor Lin Lin’s group at the Math Department of UC Berkeley, who co-led the project with Linfeng Zhang of Princeton. “Then we reorganize the data layout for bigger granularity on GPU and use data compression to significantly speedup the bottleneck. The neural network operators are optimized to the extreme, and most importantly, we successfully use half-precision in our code without losing accuracy.”

Related Publication: W. Jia, H. Wang, M. Chen, D. Lu, L. Lin, R. Car, W. E, and L. Zhang, “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning,” International Conference for High Performance Computing, Networking, Storage and Analysis, SC20 (November 2020), arXiv:2005.00223

A team from the the University of California San Diego, Argonne National Laboratory, University of Illinois Urbana-Champaign, NVIDIA Corporation, Stony Brook University, Texas Advanced Computing Center, San Diego Supercomputing Center, Rutgers University, and Brookhaven National Laboratory was awarded the 2020 ACM Gordon Bell Special Prize in HPC-Based COVID-19 Research. Image Credit: SC20

The second prize, the ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research, was awarded to team led by Rommie Amaro, professor and endowed chair of chemistry and biochemistry at the University of California San Diego, and Arvind Ramanathan, computational biologist at Argonne National Laboratory, for work building an AI-based workflow to simulate the SARS-CoV-2 virus’ spike in numerous environments, including within the SARS-CoV-2 viral envelope comprising 305 million atoms—the most comprehensive simulation of the virus performed to date.

Arvind Ramanathan; Image Credit: A. Ramanathan

The team was able to successfully optimize and scale the Nanoscale Molecular Dynamics, or NAMD, code to Summit to run their massive molecular simulations. The results of the team’s initial runs on Summit have led to discoveries of one of the mechanisms that the virus uses to evade detection as well as a characterization of interactions between the spike protein and the protein that the virus takes advantage of in human cells to gain entrance into them—the ACE2 receptor.


Rommie Amaro; Image Credit: University of California San Diego

“This is one of the first biological systems of the virus that we can learn from to drive scientific discovery,” Amaro said. “Our methods of computing allow us to get down to actually see detailed intricacies of this virus that are useful for understanding not only how it behaves but also its vulnerabilities, from a vaccine development standpoint, and a drug targeting perspective.



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The OLCF is a US Department of Energy (DOE) Office of Science User Facility located at DOE’s Oak RIdge National Laboratory.

Related Publication: Lorenzo Casalino, Abigail Dommer, Zied Gaieb, Emilia P. Barros, Terra Sztain, Surl-Hee Ahn, Anda Trifan, Alexander Brace, Anthony Bogetti, Heng Ma, Hyungro Lee, Matteo Turilli, Syma Khalid, Lillian Chong, Carlos Simmerling, David J. Hardy, Julio D. C. Maia, James C. Phillips, Thorsten Kurth, Abraham Stern, Lei Huang, John McCalpin, Mahidhar Tatineni, Tom Gibbs, John E. Stone, Shantenu Jha, Arvind Ramanathan, and Rommie E. Amaro. “AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.” To appear in International Journal of High Performance Computing Applications, 2020.

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