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高性能计算系统上电子结构代码的性能分析:以SIESTA为例

Performance analysis of electronic structure codes on HPC systems: a case study of SIESTA.

作者信息

Corsetti Fabiano

机构信息

CIC nanoGUNE, Donostia-San Sebastián, Spain.

出版信息

PLoS One. 2014 Apr 18;9(4):e95390. doi: 10.1371/journal.pone.0095390. eCollection 2014.

DOI:10.1371/journal.pone.0095390
PMID:24748385
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3991679/
Abstract

We report on scaling and timing tests of the SIESTA electronic structure code for ab initio molecular dynamics simulations using density-functional theory. The tests are performed on six large-scale supercomputers belonging to the PRACE Tier-0 network with four different architectures: Cray XE6, IBM BlueGene/Q, BullX, and IBM iDataPlex. We employ a systematic strategy for simultaneously testing weak and strong scaling, and propose a measure which is independent of the range of number of cores on which the tests are performed to quantify strong scaling efficiency as a function of simulation size. We find an increase in efficiency with simulation size for all machines, with a qualitatively different curve depending on the supercomputer topology, and discuss the connection of this functional form with weak scaling behaviour. We also analyze the absolute timings obtained in our tests, showing the range of system sizes and cores favourable for different machines. Our results can be employed as a guide both for running SIESTA on parallel architectures, and for executing similar scaling tests of other electronic structure codes.

摘要

我们报告了使用密度泛函理论进行从头算分子动力学模拟的SIESTA电子结构代码的规模和计时测试。这些测试在属于PRACE Tier-0网络的六台大型超级计算机上进行,这些超级计算机具有四种不同的架构:Cray XE6、IBM BlueGene/Q、BullX和IBM iDataPlex。我们采用一种系统策略同时测试弱缩放和强缩放,并提出一种与执行测试的核心数量范围无关的度量,以量化作为模拟大小函数的强缩放效率。我们发现所有机器的效率都随着模拟大小的增加而提高,根据超级计算机拓扑结构的不同,曲线在性质上也有所不同,并讨论了这种函数形式与弱缩放行为的联系。我们还分析了测试中获得的绝对计时,展示了适合不同机器的系统大小和核心数量范围。我们的结果既可以作为在并行架构上运行SIESTA的指南,也可以作为执行其他电子结构代码类似缩放测试的指南。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/8c3fd19c4f71/pone.0095390.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/ae43bb68855f/pone.0095390.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/137bab47c0e4/pone.0095390.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/8a390ef7b1e2/pone.0095390.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/b975f66afaef/pone.0095390.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/b2d519f77bde/pone.0095390.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/28a57e8c5374/pone.0095390.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/8c3fd19c4f71/pone.0095390.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/ae43bb68855f/pone.0095390.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/137bab47c0e4/pone.0095390.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/8a390ef7b1e2/pone.0095390.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/b975f66afaef/pone.0095390.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/b2d519f77bde/pone.0095390.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/28a57e8c5374/pone.0095390.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84f9/3991679/8c3fd19c4f71/pone.0095390.g007.jpg

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