Over the last couple of years it has been realized that the vast computational power of graphics processing units (GPUs) could be harvested for purposes other than the video game industry. This power, which at least nominally exceeds that of current CPUs by large factors, results from the relative simplicity of the GPU architectures as compared to CPUs, combined with a large number of parallel processing units on a single chip. To benefit from this setup for general computing purposes, the problems at hand need to be prepared in a way to profit from the inherent parallelism and hierarchical structure of memory accesses. In this contribution I discuss the performance potential for simulating spin models, such as the Ising model, on GPU as compared to conventional simulations on CPU.
|Journal||Computer Physics Communications|
|Publication status||Published - 2011|
Bibliographical noteThe full text is available free from the link given. The published version can be found at http://dx.doi.org10.1016/j.cpc.2010.10.031. Please note the author was working at the Institut für Physik, Johannes Gutenberg-Universität Mainz at the time of publication.
- Monte Carlo simulations
- spin models