@article { , title = {Regular lattice and small-world spin model simulations using CUDA and GPUs}, abstract = {Data-parallel accelerator devices such as Graphical Processing Units (GPUs) are providing dramatic performance improvements over evenmulti-coreCPUs for lattice-oriented applications in computational physics. Models such as the Ising and Potts models continue to play a role in investigating phase transitions on smallworld and scale-free graph structures. These models are particularly well-suited to the performance gains possible using GPUs and relatively high-level device programming languages such as NVIDIA'sComputeUnified Device Architecture (CUDA).We report on algorithms andCUDAdata-parallel programming techniques for implementingMetropolis Monte Carlo updates for the Isingmodel using bit-packing storage, and adjacency neighbour lists for various graph structures in addition to regular hypercubic lattices. We report on parallel performance gains and also memory and performance tradeoffs using GPU/CPU and algorithmic combinations.}, doi = {10.1007/s10766-010-0143-4}, eissn = {1573-7640}, issn = {0885-7458}, issue = {2}, journal = {International Journal Of Parallel Programming}, pages = {183-201}, publicationstatus = {Published}, publisher = {Springer Verlag}, url = {https://hull-repository.worktribe.com/output/470713}, volume = {39}, keyword = {Specialist Research - Other, Theoretical Computer Science, Software, Information Systems}, year = {2011}, author = {Hawick, K. A. and Leist, A. and Playne, D. P.} }