Multipath Metropolis Simulation of Classical Heisenberg Model

TL;DR

This paper explores a multipath parallel simulation approach for the classical Heisenberg model, leveraging independent random walks to efficiently utilize multi-core computing resources and simplify statistical analysis.

Contribution

It introduces a multipath simulation method that is inherently parallel and produces normally distributed outputs, improving efficiency and statistical processing over traditional single-path algorithms.

Findings

Multipath approach enables efficient parallel computation.

Simulation outputs are normally distributed, simplifying analysis.

Results demonstrate effective utilization of multi-core systems.

Abstract

Processor cores are becoming less expensive and thus more accessible. To utilize increasing number of available computing elements, good parallel algorithms are necessary. In light of these changes in contemporary computing, multipath Metropolis simulation of classical Heisenberg model is explored. In contrast to the original single-path algorithm, multipath simulation approach is inherently parallel because different random-walk paths are mutually independent. This independence enables easy and efficient harnessing of numerous cores' computing power in embarrassingly parallel algorithms. Aside form being inherently parallel, multipath simulation approach results in independent and normally distributed simulation output. Normal distribution enables simple and straightforward statistical processing. Thus, multipath simulation results can be easily computed with arbitrary and statistically known precision.