Phaseless Auxiliary-Field Quantum Monte Carlo on Graphical Processing Units

TL;DR

This paper introduces a GPU-accelerated implementation of phaseless Auxiliary-Field Quantum Monte Carlo, achieving significant speed-ups and enabling the study of larger, more complex electronic systems with improved efficiency and accuracy.

Contribution

The paper presents a novel GPU-based implementation of ph-AFQMC with algorithmic innovations that drastically reduce computation time and extend applicability to larger systems.

Findings

Achieved roughly two orders of magnitude speed-up with a single GPU.

Demonstrated near-perfect parallel efficiency across 8 GPUs.

Successfully applied to hydrogen chains and transition metal atoms for ionization potentials.

Abstract

We present an implementation of phaseless Auxiliary-Field Quantum Monte Carlo (ph-AFQMC) utilizing graphical processing units (GPUs). The AFQMC method is recast in terms of matrix operations which are spread across thousands of processing cores and are executed in batches using custom Compute Unified Device Architecture kernels and the hardware-optimized cuBLAS matrix library. Algorithmic advances include a batched Sherman-Morrison-Woodbury algorithm to quickly update matrix determinants and inverses, density-fitting of the two-electron integrals, an energy algorithm involving a high-dimensional precomputed tensor, and the use of single-precision floating point arithmetic. These strategies result in dramatic reductions in wall-times for both single- and multi-determinant trial wavefunctions. For typical calculations we find speed-ups of roughly two orders of magnitude using just a single GPU card. Furthermore, we achieve near-unity parallel efficiency using 8 GPU cards on a single node, and can reach moderate system sizes via a local memory-slicing approach. We illustrate the robustness of our implementation on hydrogen chains of increasing length, and through the calculation of all-electron ionization potentials of the first-row transition metal atoms. We compare long imaginary-time calculations utilizing a population control algorithm with our previously published correlated sampling approach, and show that the latter improves not only the efficiency but also the accuracy of the computed ionization potentials. Taken together, the GPU implementation combined with correlated sampling provides a compelling computational method that will broaden the application of ph-AFQMC to the description of realistic correlated electronic systems.