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A New Scalable Parallel Algorithm for Fock Matrix Construction

机译:Fock矩阵构造的一种新的可扩展并行算法

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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.
机译:Hartree-Fock(HF)或自洽场(SCF)计算在量子化学中被广泛使用,并且是精确电子相关方法的起点。但是,现有的算法和软件可能无法扩展到分布式机器的大量内核,尤其是在模拟中等大小的分子时。在现有代码中,HF计算分为任务。细粒度的任务更适合负载平衡,但是粗粒度的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一折衷:我们使用细粒度的任务来平衡大量内核之间的计算,但同​​时也使用一种方案将任务分配给进程以减少通信。我们特别关注HF算法中产生的Fock矩阵的分布式构造,并详细描述数据访问模式。对于我们的测试分子,我们的实现在构建Fock矩阵方面显示出比NWChem更好的可伸缩性。

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