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  3. Load-balanced Diffusion Monte Carlo Method With Lattice Regularization.
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  3. Load-balanced Diffusion Monte Carlo Method With Lattice Regularization.

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Load-balanced diffusion Monte Carlo method with lattice regularization.

Kousuke Nakano1, Sandro Sorella2, Michele Casula3

  • 1Center for Basic Research on Materials (CBRM), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.

The Journal of Chemical Physics
|November 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new load-balanced lattice regularized diffusion Monte Carlo (LRDMC) algorithm improves parallel efficiency for quantum Monte Carlo (QMC) simulations. This method enhances hardware utilization on modern supercomputers, achieving high parallel efficiency and speedup for complex calculations.

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Area of Science:

  • Computational Physics
  • Quantum Chemistry
  • High-Performance Computing

Background:

  • Ab initio quantum Monte Carlo (QMC) offers a stochastic solution to the many-body Schrödinger equation without one-body approximations.
  • Diffusion Monte Carlo (DMC), a QMC variant, reliably projects onto the ground state using the fixed-node approximation.
  • Lattice regularized diffusion Monte Carlo (LRDMC) is a practical DMC implementation but suffers from load imbalance issues in parallelization.

Purpose of the Study:

  • To develop and present a novel load-balanced LRDMC algorithm.
  • To address and mitigate the inherent load imbalance in conventional LRDMC.
  • To significantly improve weak-scaling parallel efficiency on modern architectures.

Main Methods:

  • Implementation of a new LRDMC algorithm designed for inherent load balancing.
  • Utilized ensembles of walkers (Nw) for parallelization on high-performance computing systems.
  • Tested the algorithm using binding energy calculations for a water-methane complex on the Leonardo supercomputer with NVIDIA A100 GPUs.

    • Main Results:

    • The load-balanced LRDMC algorithm yields results consistent with the conventional method.
    • Achieved high parallel efficiency (∼98%) up to 512 GPUs (Nw = 51,200).
    • Demonstrated a speedup of ×1.24 compared to conventional LRDMC with the same walker count, and ×1.18 even with reduced walkers (Nw = 400).

    Conclusions:

    • The developed load-balanced LRDMC algorithm effectively resolves load imbalance issues in parallel QMC simulations.
    • This advancement leads to superior hardware utilization and parallel efficiency on large-scale computing resources.
    • The method shows significant potential for accelerating complex quantum mechanical calculations in computational physics and chemistry.