SC17 Denver, CO

P09: Adaptive Multistep Predictor for Accelerating Dynamic Implicit Finite-Element Simulations

Authors: Kohei Fujita (University of Tokyo, RIKEN), Tsuyoshi Ichimura (University of Tokyo, RIKEN), Masashi Horikoshi (Intel Corporation), Muneo Hori (University of Tokyo, RIKEN), Lalith Maddegedara (University of Tokyo, RIKEN)

Abstract: We develop an adaptive multistep predictor for accelerating memory bandwidth-bound dynamic implicit finite-element simulations. We predict the solutions for future time steps adaptively using highly-efficient matrix-vector product kernels with multiple right-hand sides to reduce the number of iterations required in the solver. By applying the method to a conjugate gradient solver with 3 x 3 block Jacobi preconditioning, we were able to achieve a 42% speedup on a Skylake-SP Xeon Gold cluster for a typical earthquake ground motion problem. As the method enables the number of iterations, and thus the communication frequency, to be reduced, the developed solver was able to attain high size-up scalability: 80.6% up to 32,768 compute nodes on the K computer. The developed predictor can also be applied to other iterative solvers and is thus expected to be useful for wide range of dynamic implicit finite-element simulations.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf

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