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SC20-Final-Program-V2.Pdf Table of Contents ACM Gordon Bell COVID Finalist Keynote ACM Gordon Bell Finalist More Than HPC Plenary ACM Student Research Competition: Panel Graduate Posters Paper ACM Student Research Competition: Research Posters Undergraduate Posters Scientific Visualization Awards Presentation & Data Analytics Showcase Birds of a Feather SCinet Booth Sessions State of the Practice Talk Doctoral Showcase Students@SC Early Career Program Test of Time Exhibitor Forum Tutorial Exhibits Virtual Student Cluster Competition Invited Talk Workshop Job Posting ACM Gordon Bell COVID Finalist (back to top) Thursday, November 19th 10:00 am - 12:00 pm Gordon Bell COVID-19 Prize Finalist Session 1 Session Description: Enabling Rapid COVID-19 Small Molecule Drug Design Through Scalable Deep Learning of Generative Models Sam Ade Jacobs (Lawrence Livermore National Laboratory), Tim Moon (Lawrence Livermore National Laboratory), Kevin McLoughlin (Lawrence Livermore National Laboratory), Derek Jones (Lawrence Livermore National Laboratory), David Hysom (Lawrence Livermore National Laboratory), Dong H. Ahn (Lawrence Livermore National Laboratory), John Gyllenhaal (Lawrence Livermore National Laboratory), Pythagoras Watson (Lawrence Livermore National Laboratory), Felice C. Lightsone (Lawrence Livermore National Laboratory), Jonathan E. Allen (Lawrence Livermore National Laboratory), Ian Karlin (Lawrence Livermore National Laboratory), Brian Van Essen (Lawrence Livermore National Laboratory) We improved the quality and reduced the time to produce machine-learned models for use in small molecule antiviral design. Our globally asynchronous multi-level parallel training approach strong scales to all of Sierra with up to 97.7% efficiency. We trained a novel, character-based Wasserstein autoencoder that produces a higher quality model trained on 1.613 billion compounds in 23 minutes while the previous state-of-the-art takes a day on 1 million compounds. Reducing training time from a day to minutes shifts the model creation bottleneck from computer job turnaround time to human innovation time. Our implementation achieves 318 PFLOPS for 17.1% of half-precision peak. We will incorporate this model into our molecular design loop, enabling the generation of more diverse compounds: searching for novel, candidate antiviral drugs improves and reduces the time to synthesize compounds to be tested in the lab. High-Throughput Virtual Laboratory for Drug Discovery Using Massive Datasets Jens Glaser (Oak Ridge National Laboratory), Josh V. Vermaas (Oak Ridge National Laboratory), David M. Rogers (Oak Ridge National Laboratory), Jeff Larkin (Nvidia Corporation), Scott LeGrand (Nvidia Corporation), Swen Boehm (Oak Ridge National Laboratory), Matthew B. Baker (Oak Ridge National Laboratory), Aaron Scheinberg (Jubilee Development), Andreas F. Tillack (Scripps Research), Mathialakan Thavappiragasam (Oak Ridge National Laboratory), Ada Sedova (Oak Ridge National Laboratory), Oscar Hernandez (Oak Ridge National Laboratory) Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS- CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350x mean speedup over the CPU version (50x speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 58x speedup for the full pipeline reduces an initial 51 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics Lorenzo Casalino (University of California, San Diego), Abigail Dommer (University of California, San Diego), Zied Gaieb (University of California, San Diego), Emilia P. Barros (University of California, San Diego), Terra Stzain (University of California, San Diego), Surl-Hee Ahn (University of California, San Diego), Anda Trifan (University of Illinois), Alexander Brace (Argonne National Laboratory (ANL)), Anthony Bogetti (University of Pittsburgh), Heng Ma (Argonne National Laboratory (ANL)), Hyungro Lee (Rutgers University), Matteo Turilli (Rutgers University), Syma Khalid (University of Southampton), Lillian Chong (University of Pittsburgh), Carlos Simmerling (Stony Brook University), David Hardy (University of Illinois), Julio Maia (University of Illinois), James Phillips (University of Illinois), Thorsten Kurth (Nvidia Corporation), Abraham Stern (Nvidia Corporation), Lei Huang (University of Texas), John McCalpain (University of Texas), Mahidhar Tatineni (San Diego Supercomputer Center), Tom Gibbs (Nvidia Corporation), John Stone (University of Illinois), Shantenu Jha (Brookhaven National Laboratory), Arvind Ramanathan (Argonne National Laboratory (ANL)), Rommie E Amaro (University of California, San Diego) We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. A Population Data-Driven Workflow for COVID-19 Modeling and Learning Jonathan Ozik (Argonne National Laboratory (ANL)), Justin M. Wozniak (Argonne National Laboratory (ANL)), Nicholson Collier (Argonne National Laboratory (ANL)), Charles M. Macal (Argonne National Laboratory (ANL)), Mickael Binois (French Institute for Research in Computer Science and Automation (INRIA)) CityCOVID is a detailed agent-based model (ABM) that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of self-protective behaviors on viral transmissibility. Throughout the COVID- 19 epidemic, CityCOVID model outputs have been provided to city, county and state stakeholders in response to evolving decision-making priorities, incorporating emerging information on SARS- CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible and performant analytical platform for planning and crisis response. ACM Gordon Bell Finalist (back to top) Wednesday, November 18th 3:00 pm - 4:30 pm Gordon Bell Prize Finalist Session 1 Session Description: Toward Realization of Numerical Towing-Tank Tests by Wall-Resolved Large Eddy Simulation Based on 32 Billion Grid Finite-Element Computation Chisachi Kato (University of Tokyo), Yoshinobu Yamade (Mizuho Information and Research Institute Inc), Katsuhiro Nagano (Mizuho Information and Research Institute Inc), Kiyoshi Kumahata (RIKEN Center for Computational Science (R-CCS)), Kazuo Minami (RIKEN Center for Computational Science (R-CCS)), Tatsuo Nishikawa (Shipbuilding Research Centre of Japan) To realize numerical towing-tank tests by substantially shortening the time to the solution, a general-purpose finite-element flow solver, named FrontFlow/Blue (FFB), has been fully optimized so as to achieve the maximum possible sustained memory throughputs with three of its four hot kernels. As a result, a single-node sustained performance of 179.0 GFLOPS, which corresponds to 5.3% of the peak performance, has been achieved on Fugaku, the next flagship computer of Japan. A weak-scale benchmark test has confirmed FFB runs with a parallel efficiency of over 85% up to 5,505,024 compute cores, and a sustained performance of 16.7 PFLOPS has been achieved. By these, the time needed for large-eddy simulation using 32 billion grids has been significantly reduced from almost two days to only 37 min., or by a factor of 71. This has clearly indicated that a numerical towing-tank could actually be built for ship hydrodynamics in a few years. Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning Weile Jia (University of California,
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