A distributed multi-GPU ab initio density matrix renormalization group algorithm with applications to the P-cluster of nitrogenase

TL;DR

This paper introduces a novel distributed multi-GPU ab initio DMRG algorithm that significantly enhances computational capacity, enabling large-scale quantum chemistry calculations on complex transition metal clusters like the nitrogenase P-cluster.

Contribution

The paper presents the first distributed multi-GPU DMRG algorithm optimized for high-performance computing, allowing larger bond dimensions and more accurate modeling of complex transition metal systems.

Findings

Achieved bond dimension D=14000 on 48 GPUs for the P-cluster

Nearly three times larger bond dimensions than previous CPU-based calculations

Demonstrated scalability and efficiency of GPU parallelization for quantum chemistry

Abstract

The presence of many degenerate $d/f$ orbitals makes polynuclear transition metal compounds such as iron-sulfur clusters in nitrogenase challenging for state-of-the-art quantum chemistry methods. To address this challenge, we present the first distributed multi-GPU (Graphics Processing Unit) \emph{ab initio} density matrix renormalization (DMRG) algorithm, suitable for modern high-performance computing (HPC) infrastructures. The central idea is to parallelize the most computationally intensive part - the multiplication of $O(K^2)$ operators with a trial wavefunction, where $K$ is the number of spatial orbitals, by combining operator parallelism for distributing the workload with a batched algorithm for performing contractions on GPU. With this new implementation, we are able to reach an unprecedentedly large bond dimension $D=14000$ on 48 GPUs (NVIDIA A100 80 GB SXM) for an active space model (114 electrons in 73 active orbitals) of the P-cluster, which is nearly three times larger than the bond dimensions reported in previous DMRG calculations for the same system using only CPUs.