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基于GPU加速的基于图形的半经验量子化学

Maksim Kulichenko¹, Robert M Stanton¹, Cheng-Han Li¹

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

Journal of chemical theory and computation

|October 27, 2025

概括

这项研究将基于图形的电子结构理论 (SEDACS) 与GPU加速的半实证方法 (PySEQM) 结合起来,以实现高效的大规模原子模拟. 该方法实现了显著的加快速度,降低了复杂化学问题的计算成本.

科学领域:

计算化学是一种计算化学.
材料科学是一种材料科学.
量子力学就是量子力学.

背景情况:

基于图形的电子结构理论为大型原子系统提供了可扩展性.
分布式和混合式计算平台对于复杂的模拟至关重要.

研究的目的:

将基于图形的线性缩放电子结构理论 (SEDACS) 与半经验量子化学方法 (PySEQM) 结合起来.
为了利用图形处理单元 (GPU) 加速,提高计算效率.
在分布式和混合平台上实现可扩展的电子结构计算.

主要方法:

集成可扩展生态系统,驱动器和复杂化学模拟分析仪 (SEDACS) 与PySEQM代码.
在合框架内实现图形处理单元 (GPU) 加速.
分析并行效率,计算精度和通信开销.

主要成果:

在多个节点实现了高效和可扩展的电子结构计算.
对于高达1万个原子的系统,演示了数量级的加速.
验证了计算成本的降低和平行运行的有效利用.

结论:

与GPU加速相结合的SEDACS和PySEQM方法为大规模的电子结构研究提供了强大的工具.
这种方法显著降低了复杂的原子系统的计算成本.
这些发现为更容易获得和更有效的计算化学研究铺平了道路.

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