Accelerating Auxiliary-Field Quantum Monte Carlo Simulations of Solids with Graphical Processing Units

2020/03/20 by Fionn D. Malone, Shuai Zhang, Miguel A. Morales · 34 citations
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computational science #Computer science #Diamond and Carbon-based Materials Research #Electronic and Structural Properties of Oxides #Field (mathematics) #Leverage (statistics) #Mathematics #Monte Carlo method #Parallel computing #Physics #Quantum Monte Carlo #Semiconductor materials and devices #Speedup #Statistical physics #cond-mat.str-el #physics.chem-ph #physics.comp-ph

paper · pdf · doi:10.1021/acs.jctc.0c00262

published in Journal of Chemical Theory and Computation 16(7), 4286-4297 (American Chemical Society)

arxiv created 2020/03/20 · openalex publication_date 2020/05/21 · arxiv updated 2020/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

Citations

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