New Algorithm for Tensor Contractions on Multi-Core CPUs, GPUs, and Accelerators Enables CCSD and EOM-CCSD Calculations with over 1000 Basis Functions on a Single Compute Node
New Algorithm for Tensor Contractions on Multi-Core CPUs, GPUs, and Accelerators Enables CCSD and EOM-CCSD Calculations with over 1000 Basis Functions on a Single Compute Node
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DOI：
10.1002/jcc.24713
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发表时间：
2017-04-01
影响因子：
3
通讯作者：
Krylov, Anna I.
中科院分区：
文献类型：
--
作者：
Kaliman, Ilya A.;Krylov, Anna I.
提出了一种新的适用于任意对称性和稀疏性张量的硬件无关的压缩算法。该算法被实现为一个独立的开源代码libxm。该代码还集成了通用张量库libtensor和Q-Chem量子化学包。概述了算法，其实现，和基准。与其他张量软件类似，该算法利用高效的矩阵乘法库，并假设张量以块张量形式存储。该算法的显着特点是：（i）有效地重新包装的个别块成大矩阵和背部，这提供了高效的图形处理单元（GPU）启用的计算，而无需修改更高级别的代码;（ii）磁盘存储和快速存储器之间的完全异步数据传输。该算法能够在单个quad-GPU机器上使用超过1000个基函数进行单次和双次替换（CCSD和EOM-CCSD）的规范全电子耦合簇和运动方程耦合簇计算。我们表明，该算法具有预测的理论标度为规范CCSD计算，O（N-6），无论磁盘上的数据大小。(C)2017 Wiley Periodicals，Inc.
A new hardware-agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand-alone open-source code libxm. This code is also integrated with general tensor library libtensor and with the Q-Chem quantum-chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block-tensor form. The distinguishing features of the algorithm are: (i) efficient repackaging of the individual blocks into large matrices and back, which affords efficient graphics processing unit (GPU)-enabled calculations without modifications of higher-level codes; (ii) fully asynchronous data transfer between disk storage and fast memory. The algorithm enables canonical all-electron coupled-cluster and equation-of-motion coupled-cluster calculations with single and double substitutions (CCSD and EOM-CCSD) with over 1000 basis functions on a single quad-GPU machine. We show that the algorithm exhibits predicted theoretical scaling for canonical CCSD calculations, O(N-6), irrespective of the data size on disk. (C) 2017 Wiley Periodicals, Inc.