在顶点上扩展Hartree-Fock矩阵

Giuseppe M. J. Barca, David L. Poole, J. Vallejo, Melisa Alkan, C. Bertoni, Alistair P. Rendell, M. Gordon
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引用次数: 9

摘要

图形处理单元(GPU)的使用已经成为模拟大分子系统化学的战略,大多数顶级超级计算机使用GPU作为其计算能力的主要来源。本文提出了一种新的基于碎片的Hartree-Fock矩阵构建算法,该算法设计用于多gpu架构下的缩放。该算法采用了一种基于装箱壳对容器的动态负载均衡方案,将具有相同代码路径的重要壳对分批分发到不同的gpu上。这最大限度地提高了计算吞吐量和负载平衡,并消除了GPU线程发散由于整体筛选。此外,该代码使用了一种新颖的Fock消化算法来将电子排斥积分压缩到Fock矩阵中,该算法利用了所有形式的排列对称并消除了线程同步要求。该实现在Summit计算机上展示了出色的可扩展性,在4096个节点上实现了良好的强扩展性能,在612个节点上实现了线性弱扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scaling the Hartree-Fock Matrix Build on Summit
Usage of Graphics Processing Units (GPU) has become strategic for simulating the chemistry of large molecular systems, with the majority of top supercomputers utilizing GPUs as their main source of computational horsepower. In this paper, a new fragmentation-based Hartree-Fock matrix build algorithm designed for scaling on many-GPU architectures is presented. The new algorithm uses a novel dynamic load balancing scheme based on a binned shell-pair container to distribute batches of significant shell quartets with the same code path to different GPUs. This maximizes computational throughput and load balancing, and eliminates GPU thread divergence due to integral screening. Additionally, the code uses a novel Fock digestion algorithm to contract electron repulsion integrals into the Fock matrix, which exploits all forms of permutational symmetry and eliminates thread synchronization requirements. The implementation demonstrates excellent scalability on the Summit computer, achieving good strong scaling performance up to 4096 nodes, and linear weak scaling up to 612 nodes.
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