高性能多GPU分析RI-MP2能量梯度
Ryan Stocks1, Elise Palethorpe1, Giuseppe M J Barca1
1School of Computing, Australian National University, Canberra, ACT 2601, Australia.
Journal of chemical theory and computation
|March 8, 2024
概括
这项研究引入了一种新的GPU加速算法,用于计算分析能量梯度,使用识别分辨率的Møller-Plesset扰动理论 (RI-MP2). 这种方法显著减少了计算时间,并通过分子碎片化有利地扩展.
科学领域:
- 计算化学计算化学
- 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:
- 第二阶梅勒-普莱塞特扰动理论 (MP2) 对于准确的电子结构计算至关重要.
- 分析能量梯度对于几何优化和分子动力学至关重要.
- 现有的方法面临着计算瓶,特别是在大型系统中.
研究的目的:
- 为RI-MP2分析能量梯度开发一个高性能算法.
- 为了利用多个图形处理单元 (GPU) 实现显著的计算加速.
- 为更大的分子系统提供准确的梯度计算.
主要方法:
- 在Extreme Scale电子结构系统 (EXESS) 软件中实现了一种新的GPU加速算法.
- 利用GPU进行积分生成,张量形成,Z向量方程解决和梯度积累.
- 与分子碎片化框架的集成,以减少计算缩放.
主要成果:
- 在使用8个A100 GPU的节点上实现了超过80%的理论峰值浮点性能.
- 与已建立的基于CPU的方法 (Q-Chem,ORCA) 相比,证明了高达95倍的速度.
- 减少RI-MP2梯度计算,使用分子碎片化从五位数扩展到次方位数扩展.
结论:
- 开发的GPU加速RI-MP2梯度算法提供了实质性的性能提升.
- 与分子碎片化的集成提供了显著的计算节省与高精度.
- 这种方法使得更大,更复杂的分子能够进行高效,准确的电子结构计算.
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