K. A. Hawick
Regular lattice and small-world spin model simulations using CUDA and GPUs
Hawick, K. A.; Leist, A.; Playne, D. P.
D. P. Playne
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.
|Journal Article Type||Article|
|Publication Date||Apr 1, 2011|
|Journal||International Journal Of Parallel Programming|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Hawick, K. A., Leist, A., & Playne, D. P. (2011). Regular lattice and small-world spin model simulations using CUDA and GPUs. International Journal of Parallel Programming, 39(2), 183-201. doi:10.1007/s10766-010-0143-4|
|Keywords||Theoretical Computer Science; Software; Information Systems|
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