用GPU加速相对论迪拉克 - 科恩 - 沙姆方法:BERTHA和PyBERTHA的预exa规模实现
Loriano Storchi1,2, Laura Bellentani3, Jeff Hammond4
1Dipartimento di Farmacia, Università G. d'Annunzio Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.
Journal of chemical theory and computation
|March 21, 2025
概括
研究人员优化了Dirac-Kohn-Sham (DKS) 方法用于GPU,在复杂的计算中实现了显著的加速度. 这种混合CPU/GPU方法加速了科学计算,并使高效的大规模模拟成为可能.
科学领域:
- 计算物理 计算物理
- 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:
- 狄拉克-科恩-沙姆 (DKS) 方法对于相对论电子结构计算至关重要.
- 对DKS的有效计算对于解决复杂的分子系统至关重要.
- 将计算代码移植到像GPU这样的现代硬件上对于性能增长至关重要.
研究的目的:
- 在BERTHA代码中展示DKS方法计算的进步.
- 为了证明FORTRAN代码对图形处理单元 (GPU) 的高效移植.
- 通过混合CPU/GPU实现加速DKS和实时DKS (RT-TDDKS) 计算.
主要方法:
- 使用混合的OpenMP/OpenACC编程模型进行CPU/GPU实现.
- 通过OpenACC将计算密集的DKS矩阵评估 (三中心二电子积分) 卸载到GPU.
- 使用GPU优化的线性代数库 (cuBLAS,cuSOLVER).
- 使用 FORTRAN 整体内核移植基于 Python 的 RT-TDDKS 实现 (PyBERTHART).
主要成果:
- 实现了显著的加快速度:在单点DKS能量计算 (Au16) 中高达30倍,在RT-TDDKS计算 (Au8) 中高达10倍.
- 展示了一个高效的混合CPU/GPU实现,利用FORTRAN和OpenACC.
- 成功地将Python API移植到基于FORTRAN内核的GPU,用于双电子积分评估.
结论:
- 介绍的混合CPU/GPU方法为DKS计算提供了相当大的加速.
- 这项工作代表了Python API对GPU的新移植,使用FORTRAN内核进行整体评估.
- 该方法是一般的,为未来的超大规模实施铺平了道路.
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