On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

TL;DR

This paper presents a GPU-accelerated, scalable method for evaluating the exchange-correlation potential in Kohn-Sham DFT, significantly improving performance on large HPC clusters with multiple GPUs.

Contribution

It introduces a three-level parallelism scheme and batched kernel techniques for efficient XC potential evaluation on GPU clusters, enhancing existing CPU-based methods.

Findings

Achieves high performance and scalability on GPU clusters

Demonstrates significant speedup over CPU implementations

Integrates seamlessly into the NWChemEx software package

Abstract

The predominance of Kohn-Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high performance computing (HPC). With recent trends in HPC leading towards in increasing reliance on heterogeneous accelerator based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high-levels of performance which have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn-Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high-levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.