2010/06/30 by Martin Weigel
Computer Science · Physics and Astronomy · #CUDA #Computational science #Computer architecture #Computer graphics (images) #Computer science #General-purpose computing on graphics processing units #Graphics #Graphics processing unit #Parallel computing #Parallelism (grammar) #Quantum Computing Algorithms and Architecture #Quantum many-body systems #Theoretical and Computational Physics #cond-mat.stat-mech #hep-lat #physics.comp-ph
paper · pdf · doi:10.1016/j.cpc.2010.10.031
published as Comput.Phys.Commun.182:1833,2011 · 5 pages, 4 figures, elsarticle
openalex publication_date 2010/11/04 · arxiv created 2011/06/07 · arxiv updated 2011/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.