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Low communication high performance ab initio density matrix renormalization group algorithms

Zhai, Huanchen and Chan, Garnet Kin-Lic (2021) Low communication high performance ab initio density matrix renormalization group algorithms. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210323-134707908

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Abstract

There has been recent interest in the deployment of ab initio density matrix renormalization group computations on high performance computing platforms. Here, we introduce a reformulation of the conventional distributed memory ab initio DMRG algorithm that connects it to the conceptually simpler and advantageous sum of sub-Hamiltonians approach. Starting from this framework, we further explore a hierarchy of parallelism strategies, that includes (i) parallelism over the sum of sub-Hamiltonians, (ii) parallelism over sites, (iii) parallelism over normal and complementary operators, (iv) parallelism over symmetry sectors, and (v) parallelism over dense matrix multiplications. We describe how to reduce processor load imbalance and the communication cost of the algorithm to achieve higher efficiencies. We illustrate the performance of our new open-source implementation on a recent benchmark ground-state calculation of benzene in an orbital space of 108 orbitals and 30 electrons, with a bond dimension of up to 6000, and a model of the FeMo cofactor with 76 orbitals and 113 electrons. The observed parallel scaling from 448 to 2800 CPU cores is nearly ideal.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2103.09976arXivDiscussion Paper
ORCID:
AuthorORCID
Chan, Garnet Kin-Lic0000-0001-8009-6038
Additional Information:Attribution 4.0 International (CC BY 4.0). This work was supported by the US National Science Foundation (NSF) via grant CHE-1665333. HZ thanks Seunghoon Lee for providing the integrals and reference DMRG outputs for the benzene system, and Henrik R. Larsson, Zhi-Hao Cui and Tianyu Zhu for helpful discussions. The computations presented in this work were conducted on the Caltech High Performance Cluster, partially supported by a grant from the Gordon and Betty Moore Foundation.
Funders:
Funding AgencyGrant Number
NSFCHE-1665333
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20210323-134707908
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210323-134707908
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108531
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Mar 2021 20:54
Last Modified:23 Mar 2021 20:54

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