Parallel canonical Monte Carlo simulations through sequential updating of particles

Area of Science:

• Computational Physics
• Statistical Mechanics
• Molecular Simulations

Background:

• Canonical Monte Carlo (CMC) simulations typically use random particle updating, which is equivalent to sequential updating due to particle indistinguishability.
• Grand canonical Monte Carlo (GCMC) simulations benefit from sequential particle transfer for improved serial and parallel efficiency in dense systems.

Purpose of the Study:

• To propose and evaluate a parallelization method for CMC simulations using sequential particle updating.
• To reduce interprocessor communication overhead in parallel CMC simulations.

Main Methods:

• Domain decomposition techniques applied to CMC simulations.
• Sequential updating of particles within divided domains (middle and outer sections).
• Minimizing interprocessor communication by synchronizing updates after outer region processing.

Main Results:

• Demonstrated significant improvements in parallel efficiency for CMC simulations.
• Observed nearly perfect parallel efficiency gains for large systems.
• Validated the method on two- and three-dimensional Lennard-Jones fluids.

Conclusions:

• Sequential updating is an effective strategy for parallelizing CMC simulations.
• The proposed domain decomposition and sequential updating method substantially reduces communication costs.
• This approach offers a scalable and efficient way to perform large-scale molecular simulations.