Poster 64: 416-PFLOPS Fast Scalable Implicit Solver on Low-Ordered Unstructured Finite Elements Accelerated by 1.10-ExaFLOPS Kernel with Reformulated AI-Like Algorithm: For Equation-Based Earthquake Modeling

Authors: Tsuyoshi Ichimura (University of Tokyo, RIKEN), Kohei Fujita (University of Tokyo, RIKEN), Takuma Yamaguchi (University of Tokyo), Akira Naruse (Nvidia Corporation), Jack C. Wells (Oak Ridge National Laboratory), Christopher J. Zimmer (Oak Ridge National Laboratory), Tjerk P. Straatsma (Oak Ridge National Laboratory), Takane Hori (Japan Agency for Marine-Earth Science and Technology), Simone Puel (University of Texas), Thorsten W. Becker (University of Texas), Muneo Hori (Japan Agency for Marine-Earth Science and Technology), Naonori Ueda (RIKEN)

Abstract: We propose herein an approach for reformulating an equation-based modeling algorithm to an algorithm similar to that of training artificial intelligence (AI) and accelerate this algorithm using high-performance accelerators to reduce the huge computational costs encountered for physics equation-based modeling in earthquake disaster mitigation. A fast scalable equation-based implicit solver on unstructured finite elements is accelerated with a Tensor Core-enabled matrix-vector product kernel. The developed kernel attains 1.10 ExaFLOPS, leading to 416 PFLOPS for the whole solver on full Summit. This corresponds to a 75-fold speedup from a previous state-of-the-art solver running on full Piz Daint. This result could lead to breakthroughs in earthquake disaster mitigation. Our new idea in the HPC algorithm design of combining equation-based modeling with AI is expected to have broad impacts in other earth science and industrial problems.

Best Poster Finalist (BP): no

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