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Population Annealing and Large Scale Simulations in Statistical Mechanics

  • Lev ShchurEmail author
  • Lev Barash
  • Martin Weigel
  • Wolfhard Janke
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)

Abstract

Population annealing is a novel Monte Carlo algorithm designed for simulations of systems of statistical mechanics with rugged free-energy landscapes. We discuss a realization of the algorithm for the use on a hybrid computing architecture combining CPUs and GPGPUs. The particular advantage of this approach is that it is fully scalable up to many thousands of threads. We report on applications of the developed realization to several interesting problems, in particular the Ising and Potts models, and review applications of population annealing to further systems.

Keywords

Parallel algorithms Scalability Statistical mechanics Population annealing Markov Chain Monte Carlo Sequential Monte Carlo Hybrid computing architecture CPU+GPGPU 

Notes

Acknowledgment

This work was partially supported by the grant 14-21-00158 from the Russian Science Foundation and by the Landau Institute for Theoretical Physics in the framework of the tasks from the Federal Agency of Scientific Organizations. The authors acknowledge support from the European Commission through the IRSES network DIONICOS under Contract No. PIRSES-GA-2013-612707.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Landau Institute for Theoretical PhysicsChernogolovkaRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Science Center in ChernogolovkaChernogolovkaRussia
  4. 4.Applied Mathematics Research CentreCoventry UniversityCoventryUK
  5. 5.Institut für Theoretische PhysikUniversität LeipzigLeipzigGermany

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