# Load-balanced diffusion Monte Carlo method with lattice regularization

https://mdr.nims.go.jp/datasets/0c5c65f7-bc2a-4ee3-932e-b2e062c1c1a0

## File

- [194117_1_5.0296986.pdf](https://mdr.nims.go.jp/filesets/39176d62-0317-4218-88c6-1d50a5d902c5/download) ([Detail](https://mdr.nims.go.jp/filesets/39176d62-0317-4218-88c6-1d50a5d902c5.md))

## Id

0c5c65f7-bc2a-4ee3-932e-b2e062c1c1a0

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-01T07:44:06.092723Z

## Updated at

2025-12-01T23:30:15.344785Z

## Published at

2025-12-01T23:23:28.266293Z

## Doi



## First published url

https://doi.org/10.1063/5.0296986

## Date published

2025-11-21

## Recorded date published

2025-11-21

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Load-balanced diffusion Monte Carlo method with lattice regularization
  title_type: original
  lang: en

## Description

- description: Ab initio quantum Monte Carlo (QMC) is a stochastic approach for solving
    the many-body Schrödinger equation without resorting to one-body approximations.
    QMC algorithms are readily parallelizable via ensembles of Nw walkers, making
    them well suited to large-scale high-performance computing. Among the QMC techniques,
    diffusion Monte Carlo (DMC) is widely regarded as the most reliable since it provides
    the projection onto the ground state of a given Hamiltonian under the fixed-node
    approximation. One practical realization of DMC is the lattice regularized diffusion
    Monte Carlo (LRDMC) method, which discretizes the Hamiltonian within the Green’s
    function Monte Carlo framework. DMC methods—including LRDMC—employ the so-called
    branching technique to stabilize walker weights and populations. At the branching
    step, walkers must be synchronized globally; any imbalance in per-walker workload
    can leave central processing unit (CPU) or graphics processing unit (GPU) cores
    idle, thereby degrading overall hardware utilization. The conventional LRDMC algorithm
    intrinsically suffers from such load imbalance, which grows as log(Nw), rendering
    it less efficient on modern parallel architectures. In this work, we present an
    LRDMC algorithm that inherently addresses the load imbalance issue and achieves
    significantly improved weak-scaling parallel efficiency. Using the binding energy
    calculation of a water–methane complex as a test case, we demonstrated that the
    conventional and load-balanced LRDMC algorithms yield consistent results. Furthermore,
    by utilizing the Leonardo supercomputer equipped with NVIDIA A100 GPUs, we demonstrated
    that the load-balanced LRDMC algorithm can maintain extremely high parallel efficiency
    (∼98%) up to 512 GPUs (corresponding to Nw = 51 200), together with a speedup
    of ×1.24 if directly compared with the conventional LRDMC algorithm with the same
    number of walkers. The speedup stays sizable, i.e., × 1.18, even if the number
    of walkers is reduced to Nw = 400.
  description_type: abstract
  lang: und

## Creator

- name: Kousuke Nakano
  role: author
  orcid: https://orcid.org/0000-0001-7756-4355
  organization: National Institute for Materials Science
- name: Sandro Sorella
  role: author
- name: Michele Casula
  role: author

## Contact agent



## Publisher

organization: AIP Publishing

## Managing organization



## Keyword

- subject: Quantum Monte Carlo
  schema: not_defined
- subject: Variational Monte Carlo
  schema: not_defined
- subject: Diffusion Monte Carlo
  schema: not_defined
- subject: Diffusion Monte Carlo method with lattice regularization
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: The Journal of Chemical Physics
  issn: '00219606'
  volume: '163'
  issue: '19'
  article_number: '194117'

## Conference



## Related item



## Funding

- identifier: JPMXS0320220025
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJPR24J9
  funder_name: Japan Science and Technology Agency
- identifier: EHPC-EXT-2024E01-064
  funder_name: European High Performance Computing Joint Undertaking
- identifier: HANAMI project (Hpc AlliaNce for Applications and supercoMputing Innovation)
  funder_name: European High Performance Computing Joint Undertaking

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## Fileset

- id: 39176d62-0317-4218-88c6-1d50a5d902c5
  filename: 194117_1_5.0296986.pdf
  content_type: application/pdf
  size: 6038755
  md5: dbbff3d6c0cf26dd372e0ad2b68c7736

## Thumbnail

fileset_id: 39176d62-0317-4218-88c6-1d50a5d902c5
filename: 194117_1_5.0296986.pdf