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

• URL: http://arxiv.org/abs/2311.02854v2
• Date: Thu, 21 Dec 2023 12:03:30 GMT
• Title: A distributed multi-GPU ab initio density matrix renormalization group algorithm with applications to the P-cluster of nitrogenase
• Authors: Chunyang Xiang, Weile Jia, Wei-Hai Fang, Zhendong Li
• Abstract summary: We present the first distributed multi- GPU (Graphics Processing Unit) emphab initio density matrix renormalization (DMRG) algorithm. We are able to reach an unprecedentedly large bond dimension $D=14000$ on 48 GPU. This is nearly three times larger than the bond dimensions reported in previous DMRG calculations for the same system using only CPUs.
• Score: 1.7444066202370399
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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