Massively parallel multicanonical simulations

Jonathan Gross, Johannes Zierenberg, Martin Weigel, Wolfhard Janke

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
25 Downloads (Pure)


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
Early online date13 Nov 2017
Publication statusPublished - Mar 2018

Bibliographical note

source code available at


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


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