Monte Carlo methods for massively parallel computers

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

9 Citations (Scopus)


Applications that require substantial computational resources today cannot avoid the use of heavily parallel machines. Embracing the opportunities of parallel computing and especially the possibilities provided by a new generation of massively parallel accelerator devices such as GPUs, Intel's Xeon Phi or even FPGAs enables applications and studies that are inaccessible to serial programs. Here we outline the opportunities and challenges of massively parallel computing for Monte Carlo simulations in statistical physics, with a focus on the simulation of systems exhibiting phase transitions and critical phenomena. This covers a range of canonical ensemble Markov chain techniques as well as generalized ensembles such as multicanonical simulations and population annealing. While the examples discussed are for simulations of spin systems, many of the methods are more general and moderate modifications allow them to be applied to other lattice and off-lattice problems including polymers and particle systems. We discuss important algorithmic requirements for such highly parallel simulations, such as the challenges of random-number generation for such cases, and outline a number of general design principles for parallel Monte Carlo codes to perform well.
Original languageEnglish
Title of host publicationOrder, disorder and criticality
EditorsYurij Holovatch
Place of PublicationSingapore
PublisherWorld Scientific
Number of pages70
Publication statusPublished - 1 Jan 2018

Bibliographical note

Ising Lecture 2016 delivered at ICMP, Lviv, to appear in "Order, disorder and criticality", vol. 5, ed. Yu. Holovatch


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


Dive into the research topics of 'Monte Carlo methods for massively parallel computers'. Together they form a unique fingerprint.

Cite this