GPU-based simulation of the long-range Potts model via parallel tempering

We discuss the efficiency of parallelization on graphical processing units (GPUs) for the simulation of the one-dimensional Potts model with long-range interactions via parallel tempering. We investigate the behavior of some thermodynamic properties, such as equilibrium energy and magnetization, critical temperatures as well as the separation between the first- and second-order regimes. By implementing multispin coding techniques and an efficient parallelization of the interaction energy computation among threads, the GPU-accelerated approach reached speedup factors of up to 37.