High performance implementations of the 2D Ising model on GPUs

Published: 16 Jul 2020 | Version 1 | DOI: 10.17632/xrb9xtkbcp.1
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Description of this data

We present and make available novel implementations of the two-dimensional Ising model that is used as a benchmark to show the computational capabilities of modern Graphic Processing Units (GPUs). The rich programming environment now available on GPUs and flexible hardware capabilities allowed us to quickly experiment with several implementation ideas: a simple stencil-based algorithm, recasting the stencil operations into matrix multiplies to take advantage of Tensor Cores available on NVIDIA GPUs, and a highly optimized multi-spin coding approach. Using the managed memory API available in CUDA allows for simple and efficient distribution of these implementations across a multi-GPU NVIDIA DGX-2 server. We show that even a basic GPU implementation can outperform current results published on TPUs (Yang et al., 2019) and that the optimized multi-GPU implementation can simulate very large lattices faster than custom FPGA solutions (Ortega-Zamorano et al., 2016).

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This data is associated with the following publication:

High performance implementations of the 2D Ising model on GPUs

Published in: Computer Physics Communications

Latest version

  • Version 1

    2020-07-16

    Published: 2020-07-16

    DOI: 10.17632/xrb9xtkbcp.1

    Cite this dataset

    Romero, Joshua; Bisson, Mauro; Fatica, Massimiliano; Bernaschi, Massimo (2020), “High performance implementations of the 2D Ising model on GPUs”, Mendeley Data, v1 http://dx.doi.org/10.17632/xrb9xtkbcp.1

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Categories

Statistical Physics, Thermodynamics, Computational Physics

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