### Abstract

Original language | English |
---|---|

Journal | Journal of Physics: Conference Series |

Volume | 921 |

DOIs | |

Publication status | Published - 15 Dec 2017 |

### Fingerprint

### Cite this

*Journal of Physics: Conference Series*,

*921*. https://doi.org/10.1088/1742-6596/921/1/012017

**Population annealing: Massively parallel simulations in statistical physics.** / Weigel, Martin; Barash, Lev Yu; Borovský, Michal; Janke, Wolfhard; Shchur, Lev N.

Research output: Contribution to journal › Article

*Journal of Physics: Conference Series*, vol. 921. https://doi.org/10.1088/1742-6596/921/1/012017

}

TY - JOUR

T1 - Population annealing: Massively parallel simulations in statistical physics

AU - Weigel, Martin

AU - Barash, Lev Yu

AU - Borovský, Michal

AU - Janke, Wolfhard

AU - Shchur, Lev N.

PY - 2017/12/15

Y1 - 2017/12/15

N2 - The canonical technique for Monte Carlo simulations in statistical physics is importance sampling via a suitably constructed Markov chain. While such approaches are quite successful, they are not particularly well suited for parallelization as the chain dynamics is sequential, and if replicated chains are used to increase statistics each of them relaxes into equilibrium with an intrinsic time constant that cannot be reduced by parallel work. Population annealing is a sequential Monte Carlo method that simulates an ensemble of system replica under a cooling protocol. The population element makes it naturally well suited for massively parallel simulations, and bias can be systematically reduced by increasing the population size. We present an implementation of population annealing on graphics processing units and discuss its behavior for different systems undergoing continuous and first-order phase transitions.

AB - The canonical technique for Monte Carlo simulations in statistical physics is importance sampling via a suitably constructed Markov chain. While such approaches are quite successful, they are not particularly well suited for parallelization as the chain dynamics is sequential, and if replicated chains are used to increase statistics each of them relaxes into equilibrium with an intrinsic time constant that cannot be reduced by parallel work. Population annealing is a sequential Monte Carlo method that simulates an ensemble of system replica under a cooling protocol. The population element makes it naturally well suited for massively parallel simulations, and bias can be systematically reduced by increasing the population size. We present an implementation of population annealing on graphics processing units and discuss its behavior for different systems undergoing continuous and first-order phase transitions.

U2 - 10.1088/1742-6596/921/1/012017

DO - 10.1088/1742-6596/921/1/012017

M3 - Article

VL - 921

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

ER -