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在GPU集群上更快的自洽场(SCF)计算

Faster Self-Consistent Field (SCF) Calculations on GPU Clusters.

作者信息

Barca Giuseppe M J, Alkan Melisa, Galvez-Vallejo Jorge L, Poole David L, Rendell Alistair P, Gordon Mark S

机构信息

School of Computing, Australian National University, Canberra, ACT 2601, Australia.

Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, United States.

出版信息

J Chem Theory Comput. 2021 Dec 14;17(12):7486-7503. doi: 10.1021/acs.jctc.1c00720. Epub 2021 Nov 15.

Abstract

A novel implementation of the self-consistent field (SCF) procedure specifically designed for high-performance execution on multiple graphics processing units (GPUs) is presented. The algorithm offloads to GPUs the three major computational stages of the SCF, namely, the calculation of one-electron integrals, the calculation and digestion of electron repulsion integrals, and the diagonalization of the Fock matrix, including SCF acceleration via DIIS. Performance results for a variety of test molecules and basis sets show remarkable speedups with respect to the state-of-the-art parallel GAMESS CPU code and relative to other widely used GPU codes for both single and multi-GPU execution. The new code outperforms all existing multi-GPU implementations when using eight V100 GPUs, with speedups relative to Terachem ranging from 1.2× to 3.3× and speedups of up to 28× over QUICK on one GPU and 15× using eight GPUs. Strong scaling calculations show nearly ideal scalability up to 8 GPUs while retaining high parallel efficiency for up to 18 GPUs.

摘要

本文提出了一种自洽场(SCF)程序的新颖实现方式,该方式专门为在多个图形处理单元(GPU)上进行高性能执行而设计。该算法将SCF的三个主要计算阶段卸载到GPU上,即单电子积分的计算、电子排斥积分的计算和处理,以及福克矩阵的对角化,包括通过DIIS进行的SCF加速。针对各种测试分子和基组的性能结果表明,相对于最先进的并行GAMESS CPU代码以及其他广泛使用的用于单GPU和多GPU执行的GPU代码,该算法具有显著的加速效果。当使用八个V100 GPU时,新代码优于所有现有的多GPU实现方式,相对于Terachem的加速比在1.2倍至3.3倍之间,在一个GPU上相对于QUICK的加速比高达28倍,使用八个GPU时加速比为15倍。强缩放计算表明,在多达8个GPU的情况下,几乎具有理想的可扩展性,同时在多达18个GPU的情况下保持高并行效率。

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