使用GPU加速合集群计算:对使用OpenMP指令的异质计算架构实施密度拟合的CCSD方法
Dipayan Datta1, Mark S Gordon1
1Department of Chemistry and Ames Laboratory, Iowa State University, 2416 Pammel Drive, Ames, Iowa 50011-2416, United States.
Journal of chemical theory and computation
|October 25, 2023
概括
本研究介绍了一种新的算法,用于使用图形处理单元 (GPU) 的合集群单,双和扰动三次计算. 混合CPU-GPU方法显著加速计算化学,使得更大的分子模拟.
科学领域:
- 计算化学的计算化学
- 高性能计算 高性能计算
- 量子化学 是一个量子化学.
背景情况:
- 结合集群单,双和扰动三重 [CCSD(T]是一种高精度的量子化学方法.
- 大规模的CCSD (T) 计算在计算上要求很高,这限制了它们在较小的系统中的应用.
- 不同质的计算平台为加速这些计算提供了潜力.
研究的目的:
- 在混合CPU-GPU架构上开发和实施RI-CCSD (T) 计算的高效算法.
- 为了利用OpenMP指令来实现GPU卸载计算密集型术语.
- 为了证明开发代码的性能和可扩展性,在超级计算机上使用超级计算机和超级计算机.
主要方法:
- 在CCSD中进行身份解决 (RI) 接近的算法开发 (T).
- 使用OpenMP.CCSD进行RI-CCSD振幅方程的GPU卸载,以及使用OpenMP.CCSD进行扰动三倍校正.
- 使用加速数学库 (cuBLAS/hipBLAS) 进行张量收缩.
- 数据切割和最小化CPU-GPU数据传输的策略.
主要成果:
- 与只有CPU的实现相比,实现了GPU卸载RI-CCSD术语的4-8×加快速度.
- 证明了与分子大小相关的扰动性三倍校正的加速增加,达到C66H20的5.7×.
- 在使用12288个AMDGPU并行效率为83.1%的Frontier超级计算机上,在7分钟内启用了C60的RI-CCSD (T) 的计算.
结论:
- 混合CPU-GPU RI-CCSD (T) 算法提供了显著的计算加速.
- 这种方法可以对更大的分子系统进行精确的量子化学计算.
- 这项工作为现代超级计算机架构的常规高精度电子结构计算铺平了道路.
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