GPU accelerated population annealing algorithm

Lev Yu Barash, Martin Weigel, Michal Borovský, Wolfhard Janke, Lev N. Shchur

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    42 Citations (Scopus)
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    Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures.

    Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computer Physics Communications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Physics Communications, [220, (2017)] DOI: 10.1016/j.cpc.2017.06.020

    © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    Original languageEnglish
    Pages (from-to)314-350
    Number of pages37
    JournalComputer Physics Communications
    Early online date1 Jul 2017
    Publication statusPublished - Nov 2017

    Bibliographical note

    12 pages, 3 figures and 5 tables, code at


    • physics.comp-ph
    • cond-mat.dis-nn
    • cond-mat.soft
    • cond-mat.stat-mech


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