Massively parallel multicanonical simulations

Jonathan Gross, Johannes Zierenberg, Martin Weigel, Wolfhard Janke

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)
    70 Downloads (Pure)

    Abstract

    Generalized-ensemble Monte Carlo simulations such as the multicanonical method and similar techniques are among the most efficient approaches for simulations of systems undergoing discontinuous phase transitions or with rugged free- energy landscapes. As Markov chain methods, they are inherently serial computationally. It was demonstrated recently, however, that a combination of independent simulations that communicate weight updates at variable intervals allows for the efficient utilization of parallel computational resources for multicanonical simulations. Implementing this approach for the many-thread architecture provided by current generations of graphics processing units (GPUs), we show how it can be efficiently employed with of the order of $10^4$ parallel walkers and beyond, thus constituting a versatile tool for Monte Carlo simulations in the era of massively parallel computing. We provide the fully documented source code for the approach applied to the paradigmatic example of the two-dimensional Ising model as starting point and reference for practitioners in the field.
    Original languageEnglish
    Pages (from-to)387-395
    Number of pages8
    JournalComputer Physics Communications
    Volume224
    Early online date13 Nov 2017
    DOIs
    Publication statusPublished - Mar 2018

    Bibliographical note

    source code available at https://github.com/CQT-Leipzig/cudamuca

    Keywords

    • GPU
    • Parallel computing
    • Monte Carlo simulations
    • Multicanonical
    • Ising model

    Fingerprint

    Dive into the research topics of 'Massively parallel multicanonical simulations'. Together they form a unique fingerprint.

    Cite this