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大规模并行算法及万亿核众核超级计算机上 RI-MP2 能量计算的实现。

Massively parallel algorithm and implementation of RI-MP2 energy calculation for peta-scale many-core supercomputers.

机构信息

Computational Molecular Science Research Team, RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan.

NVIDIA Corporation, 2-11-7 Akasaka, Minato-ku, Tokyo, 107-0052, Japan.

出版信息

J Comput Chem. 2016 Nov 15;37(30):2623-2633. doi: 10.1002/jcc.24491. Epub 2016 Sep 16.

Abstract

A new parallel algorithm and its implementation for the RI-MP2 energy calculation utilizing peta-flop-class many-core supercomputers are presented. Some improvements from the previous algorithm (J. Chem. Theory Comput. 2013, 9, 5373) have been performed: (1) a dual-level hierarchical parallelization scheme that enables the use of more than 10,000 Message Passing Interface (MPI) processes and (2) a new data communication scheme that reduces network communication overhead. A multi-node and multi-GPU implementation of the present algorithm is presented for calculations on a central processing unit (CPU)/graphics processing unit (GPU) hybrid supercomputer. Benchmark results of the new algorithm and its implementation using the K computer (CPU clustering system) and TSUBAME 2.5 (CPU/GPU hybrid system) demonstrate high efficiency. The peak performance of 3.1 PFLOPS is attained using 80,199 nodes of the K computer. The peak performance of the multi-node and multi-GPU implementation is 514 TFLOPS using 1349 nodes and 4047 GPUs of TSUBAME 2.5. © 2016 Wiley Periodicals, Inc.

摘要

本文提出了一种新的并行算法及其在利用 peta-flop 级多核超级计算机进行 RI-MP2 能量计算中的实现。与之前的算法(J. Chem. Theory Comput. 2013, 9, 5373)相比,该算法进行了一些改进:(1)采用两级分层并行化方案,可使用超过 10000 个消息传递接口(MPI)进程;(2)采用新的数据通信方案,减少网络通信开销。本文提出了一种针对中央处理器(CPU)/图形处理单元(GPU)混合超级计算机的本算法的多节点和多 GPU 实现。新算法及其在 K 计算机(CPU 集群系统)和 TSUBAME 2.5(CPU/GPU 混合系统)上的实现的基准测试结果表明其具有高效性。使用 K 计算机的 80199 个节点可达到 3.1 PFLOPS 的峰值性能。使用 TSUBAME 2.5 的 1349 个节点和 4047 个 GPU,可实现多节点和多 GPU 实现的峰值性能为 514 TFLOPS。© 2016 Wiley Periodicals, Inc.

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