适应性采样配置交互方法的并行分布式内存实现
David B Williams-Young1, Norm M Tubman2, Carlos Mejuto-Zaera3
1Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
The Journal of chemical physics
|June 1, 2023
概括
本研究引入了适应性采样配置交互 (ASCI) 的新并行实现,即选择性配置交互 (sCI) 方法. 高效的并行实现了量子系统迄今为止最大的变量ASCI计算.
科学领域:
- 量子化学是一种量子化学.
- 计算物理学的计算物理.
- 高性能计算的高性能计算.
背景情况:
- 量子系统模拟利用多种方法,其中合集群和选择配置交互 (sCI) 是突出的.
- 高性能计算 (HPC) 的进步推动了许多量子模拟方法的适应.
- 对大规模并行架构的sCI方法的开发仍然是一个未经探索的领域.
研究的目的:
- 介绍SCI方法的自适应采样配置交互 (ASCI) 方法的并行分布式内存实现.
- 在ASCI中解决决定性搜索,选择,哈密尔顿式形成和自值计算中的关键并行化挑战.
- 在并行计算资源上实现更大,更复杂的量子系统模拟.
主要方法:
- 适应性采样配置交互 (ASCI) 方法的并行分布式内存实现.
- 在决定性搜索过程中,在负载平衡中应用记忆效率的决定性约束.
- 在大规模并行系统上使用ASCI方法的变异自值计算.
主要成果:
- 在最多16384个CPU上,证明了ASCI计算的近最佳加速度.
- 成功地执行了迄今为止最大的Cr2分子 (24电子,30轨道) 的变化ASCI计算,涉及多达3x10^8的决定因素.
- 验证了并行ASCI实施的效率和可扩展性.
结论:
- 开发的平行ASCI实现显著提升了模拟大型量子系统的能力.
- 这项工作克服了关键的并行化障碍,使sCI方法在HPC环境中更容易使用.
- 提出的方法为解决更大,更复杂的量子力学问题铺平了道路.
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