Malone Fionn D, Zhang Shuai, Morales Miguel A
Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
J Chem Theory Comput. 2020 Jul 14;16(7):4286-4297. doi: 10.1021/acs.jctc.0c00262. Epub 2020 Jun 17.
We outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state systems. By exploiting conservation of crystal momentum in the one- and two-electron integrals, we show how to efficiently formulate the algorithm to best utilize current GPU architectures. We provide a detailed description of different optimization strategies and profile our implementation relative to standard approaches, demonstrating a factor of 40 speedup over a CPU implementation. With this increase in computational power, we demonstrate the ability of AFQMC to systematically converge solid state calculations with respect to basis set and system size by computing the cohesive energy of carbon in the diamond structure to within 0.02 eV of the experimental result.
我们概述了辅助场量子蒙特卡罗（AFQMC）如何利用图形处理单元（GPU）来加速固态系统的模拟。通过利用单电子和双电子积分中的晶体动量守恒，我们展示了如何有效地制定算法以最佳利用当前的GPU架构。我们详细描述了不同的优化策略，并将我们的实现与标准方法进行了性能分析，结果表明相对于CPU实现有40倍的加速。随着计算能力的提升，我们通过计算金刚石结构中碳的内聚能，使其与实验结果相差在0.02 eV以内，证明了AFQMC能够在基组和系统大小方面系统地收敛固态计算。
