GPU acceleration of Swendsen–Wang dynamics

Tristan Protzman () and Joel Giedt
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Tristan Protzman: Department of Physics, Lehigh University, Bethlehem, PA 18015, USA
Joel Giedt: ��Department of Physics, Applied Physics and Astronomy Rensselaer Polytechnic Institute, Troy, NY 12180, USA

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 01, 1-11

Abstract: When simulating a lattice system near its critical temperature, local algorithms for modeling the system’s evolution can introduce very large autocorrelation times into sampled data. This critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen–Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA’s CUDA framework. A 17 and 90 fold increase in the computational rate was, respectively, experienced when measured against the equivalent algorithm implemented in serial code.

Keywords: Cluster algorithms; GPU acceleration (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124500098

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