Analysis of Vector Particle-In-Cell (VPIC) memory usage optimizations on cutting-edge computer architectures

Tan, Nigel Phillip; Bird, Robert (Bob) Francis; Chen, Guangye; Luedtke, Scott Vernon; Albright, Brian James; Taufer, Michela

doi:10.1016/j.jocs.2022.101566

Analysis of Vector Particle-In-Cell (VPIC) memory usage optimizations on cutting-edge computer architectures

Journal Article · Mon Jan 31 00:00:00 EST 2022 · Journal of Computational Science

DOI:https://doi.org/10.1016/j.jocs.2022.101566· OSTI ID:1844166

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Univ. of Tennessee, Knoxville, TN (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Vector Particle-In-Cell (VPIC) is one of the fastest plasma simulation codes in the world, with particle numbers ranging from one trillion on the first petascale system, Roadrunner, to ten trillion particles on the more recent Blue Waters supercomputer. As supercomputers continue to grow rapidly in size, so too does the gap between computing capability and memory capability. Current memory systems limit VPIC simulations greatly as the maximum number of particles that can be simulated directly depends on the available memory. In this study, we present a suite of VPIC memory optimizations (i.e., particle weight, half-precision, and fixed-point optimizations) that enable a significant increase in the number of particles in VPIC simulations. Here, we assess the optimizations’ impact on memory and runtime performance for a suite of cutting-edge computer architectures such has the NVIDIA V100 GPU, the IBM Power9, and the Fujitsu A64FX architectures. Our optimizations enable a 31.25% reduction in memory usage and up to 40% increase in the number of particles. This paper extends our work on developing particle storage format optimizations Tan et al.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: IBM; National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1844166

Report Number(s):: LA-UR-21-31487

Journal Information:: Journal of Computational Science, Journal Name: Journal of Computational Science Vol. 60; ISSN 1877-7503

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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