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Regular Lattice and Small-World Spin Model Simulations Using CUDA and GPUs

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Abstract

Data-parallel accelerator devices such as Graphical Processing Units (GPUs) are providing dramatic performance improvements over even multi-core CPUs 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 small-world 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’s Compute Unified Device Architecture (CUDA). We report on algorithms and CUDA data-parallel programming techniques for implementing Metropolis Monte Carlo updates for the Ising model 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.

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Authors and Affiliations

  1. Institute of Information and Mathematical Sciences, Massey University, Albany, North Shore, 102-904, Auckland, New Zealand

    K. A. Hawick, A. Leist & D. P. Playne

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  1. K. A. Hawick
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  2. A. Leist
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  3. D. P. Playne
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Correspondence to K. A. Hawick.

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Hawick, K.A., Leist, A. & Playne, D.P. Regular Lattice and Small-World Spin Model Simulations Using CUDA and GPUs. Int J Parallel Prog 39, 183–201 (2011). https://doi.org/10.1007/s10766-010-0143-4

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  • DOI: https://doi.org/10.1007/s10766-010-0143-4

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