Monte Carlo Simulations of Random Frustrated Systems on Graphics Processing Units

We study the implementation of the classical Monte Carlo simulation for random frustrated models using the multithreaded computing environment provided by the the Compute Unified Device Architecture (CUDA) on modern Graphics Processing Units (GPU) with hundreds of cores and high memory bandwidth. The key for optimizing the performance of the GPU computing is in the proper handling of the data structure. Utilizing the multi-spin coding, we obtain an efficient GPU implementation of the parallel tempering Monte Carlo simulation for the Edwards-Anderson spin glass model. In the typical simulations, we find over two thousand times of speed-up over the single threaded CPU implementation.