Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure
Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure
复制标题
DOI：
10.1109/ipdps49936.2021.00062
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
T. Hérault;Y. Robert;G. Bosilca;R. Harrison;C. Lewis;Edward F. Valeev;J. Dongarra
中科院分区：
文献类型：
--
作者：
T. Hérault;Y. Robert;G. Bosilca;R. Harrison;C. Lewis;Edward F. Valeev;J. Dongarra
许多科学模拟领域（化学、凝聚态物理、数据科学）越来越多地避开密集张量，而采用块稀疏张量，有时还采用额外的结构（递归层次、秩稀疏等）。使用块稀疏张量数据的分布式存储器并行计算对于最小化求解时间（例如，以研究动态问题或用于实时分析），并适应实际大小的问题，所述实际大小的问题太大而不能装入配备有加速器的单个节点的主机/设备存储器中。不幸的是，这种不规则的数据结构的计算是一个占主导地位的命令式，大容量同步并行编程模型的匹配差。在本文中，我们专注于块稀疏张量代数的关键元素，即二进制张量收缩，并报告使用以任务为中心的PaRSEC运行时的高效和可扩展的实现。高性能的块稀疏张量收缩的首脑会议上的超级计算机的合成数据，以及为真实的数据参与电子结构模拟的前所未有的规模。
Many domains of scientific simulation (chemistry, condensed matter physics, data science) increasingly eschew dense tensors for block-sparse tensors, sometimes with additional structure (recursive hierarchy, rank sparsity, etc.). Distributed-memory parallel computation with block-sparse tensorial data is paramount to minimize the time-to-solution (e.g., to study dynamical problems or for real-time analysis) and to accommodate problems of realistic size that are too large to fit into the host/device memory of a single node equipped with accelerators. Unfortunately, computation with such irregular data structures is a poor match to the dominant imperative, bulk-synchronous parallel programming model. In this paper, we focus on the critical element of block-sparse tensor algebra, namely binary tensor contraction, and report on an efficient and scalable implementation using the task-focused PaRSEC runtime. High performance of the block-sparse tensor contraction on the Summit supercomputer is demonstrated for synthetic data as well as for real data involved in electronic structure simulations of unprecedented size.