GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems

TL;DR

This paper introduces a GPU-accelerated implementation of the Swendsen-Wang multi-cluster algorithm for 2D classical spin systems, achieving significant speedups over CPU calculations.

Contribution

The authors adapt and optimize cluster labeling algorithms on CUDA for the Swendsen-Wang algorithm, extending it to vector spin models like the q-state clock model.

Findings

Achieved 2.51 ns per spin flip for q=2 Potts model on GTX580.

Achieved 2.42 ns per spin flip for q=6 clock model on GTX580.

GPU implementation is over 12 times faster than CPU for q=2 Potts model.

Abstract

We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to the system of vector spins, the q-state clock model, with the idea of embedded cluster. We test the performance, and the calculation time on GTX580 is obtained as 2.51 nano sec per a spin flip for the q=2 Potts model (Ising model) and 2.42 nano sec per a spin flip for the q=6 clock model with the linear size L=4096 at the critical temperature, respectively. The computational speed for the q=2 Potts model on GTX580 is 12.4 times as fast as the calculation speed on a current CPU core. That for the q=6 clock model on GTX580 is 35.6 times as fast as the calculation speed on a current CPU core.