A New Scalable Parallel Algorithm for Fock Matrix Construction

Xing Liu, Aftab Patel, Edmond Chow
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引用次数: 23

Abstract

Hartree-Fock (HF) or self-consistent field (SCF) calculations are widely used in quantum chemistry, and are the starting point for accurate electronic correlation methods. Existing algorithms and software, however, may fail to scale for large numbers of cores of a distributed machine, particularly in the simulation of moderately-sized molecules. In existing codes, HF calculations are divided into tasks. Fine-grained tasks are better for load balance, but coarse-grained tasks require less communication. In this paper, we present a new parallelization of HF calculations that addresses this trade-off: we use fine grained tasks to balance the computation among large numbers of cores, but we also use a scheme to assign tasks to processes to reduce communication. We specifically focus on the distributed construction of the Fock matrix arising in the HF algorithm, and describe the data access patterns in detail. For our test molecules, our implementation shows better scalability than NWChem for constructing the Fock matrix.
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Fock矩阵构造的一种新的可扩展并行算法
Hartree-Fock (HF)或自洽场(SCF)计算在量子化学中广泛应用,是精确电子相关方法的起点。然而,现有的算法和软件可能无法对分布式机器的大量核心进行扩展,特别是在模拟中等大小的分子时。在现有代码中,高频计算被划分为多个任务。细粒度任务更适合于负载平衡,但粗粒度任务需要较少的通信。在本文中,我们提出了一种新的HF计算并行化,解决了这种权衡:我们使用细粒度任务来平衡大量核心之间的计算,但我们也使用一种方案将任务分配给进程以减少通信。重点讨论了高频算法中出现的Fock矩阵的分布式构造,并详细描述了其数据访问模式。对于我们的测试分子,我们的实现在构建Fock矩阵方面表现出比NWChem更好的可扩展性。
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