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在GPU集群上构建高性能Fock矩阵的先进技术
Elise Palethorpe1, Ryan Stocks1, Giuseppe M J Barca2
1School of Computing, Australian National University, Canberra, ACT 2601, Australia.
Journal of chemical theory and computation
|November 25, 2024
概括
两个新的多GPU算法,opt-UM和opt-Brc,加速了用于电子结构计算的Fock矩阵构造. 这些优化的方法显著优于现有的GPU和CPU实现,提供了大量的加速度和更好的功率效率.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 高性能计算 高性能计算
背景情况:
- 福克矩阵构造是电子结构计算中的关键步骤.
- 现有的算法面临性能瓶,特别是在大型系统和先进的基础集.
- 优化福克矩阵构造对于推进计算化学能力至关重要.
研究的目的:
- 开发和介绍两种新的,优化的多GPU算法用于Fock矩阵构造.
- 为了提高Hartree-Fock计算的计算效率和可扩展性.
- 在现有的CPU和GPU实现中提供显著的加快速度.
主要方法:
- 实现了两个优化的多GPU算法:opt-UM和opt-Brc.
- 纳入先进技术:改进了整体选,稀疏性/对称性利用和线性缩放交换矩阵组装.
- 在使用极端规模电子结构系统 (EXESS) 的NVIDIA A100 GPU上进行基准测试.
- 测试各种系统类型 (线性,球形) 和基础集 (双ζ,三ζ).
主要成果:
- 与TeraChem,QUICK,GPU4PySCF,LibIntX,ORCA和Q-Chem相比,这些算法表现出更高的性能.
- 与TeraChem,QUICK和GPU4PySCF相比,观察到的平均加速度分别为1.4×,8.4×和9.4×.
- 在单个节点上实现了高达42×和31×的加速度,而不是ORCA和Q-Chem.
- 在四个GPU上选择Brc的并行效率超过91%.
- 功率效率提高了多达18×.
结论:
- 开发的opt-UM和opt-Brc算法代表了福克矩阵构造的重大进步.
- 这些算法为电子结构计算提供了显著的性能提升和更好的可扩展性.
- 优化的方法为更高效和更强大的计算化学研究铺平了道路.
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