为现代分布式内存 Tensor 软件生成结合集群代码
Jan Brandejs1, Johann Pototschnig1, Trond Saue1
1Laboratoire de Chimie et Physique Quantique, UMR 5626 CNRS - Université de Toulouse, 118 route de Narbonne, Toulouse F-31062, France.
Journal of chemical theory and computation
|July 18, 2025
概括
在GPU上开发高效的高性能计算 (HPC) 软件用于合集群 (CC) 计算是复杂的. 这项工作引入了"tenpi",用于自动生成CC代码的框架,提高了复杂分子模拟的可扩展性和可访问性.
科学领域:
- 计算化学计算化学
- 高性能计算 (HPC) 是一种高性能计算.
- 量子化学 是一个量子化学.
背景情况:
- 基于GPU的高性能计算 (HPC) 平台上的合集群 (CC) 计算的高效执行受到异质硬件结构的阻碍.
- 将软件适应这些结构需要大量的人工时间,需要系统化的高性能代码开发,特别是对于更高级的CC方法.
研究的目的:
- 为了应对高效的张量对称性捕获和硬件抽象在开发一般顺序合集群 (CC) 代码生成器的挑战.
- 介绍一个新的,开源的模块化张量框架的设计",tenpi",用于CC代码开发.
主要方法:
- 通过编译器/翻译器开发一个高级问题表示,并通过编译器/翻译器将其翻译为低级硬件指令.
- 设计软件以捕获关键张量对称性,同时保持硬件抽象.
- 在"tenpi"框架内整合图形导数,可视化,符号代数和中间优化.
主要成果:
- 系统生成的代码在使用ExaTENSOR分布式内存张量库在高达1200个GPU上展示了出色的弱缩放.
- "tenpi"框架支持多个张量后端,并促进更高阶的CC功能.
- 将"tenpi"集成到DIRAC代码的ExaCorr模块中可以提高相对论分子计算.
结论:
- 开发的通用CC代码生成器和"tenpi"框架显著提高了现代HPC平台上CC计算的效率和可访问性.
- "tenpi"为先进的量子化学计算提供了强大的模块化解决方案,在大规模并行系统上实现了更高阶的CC方法.
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