# GPU accelerated population annealing algorithm

## TL;DR

This paper presents a highly optimized GPU implementation of the population annealing algorithm, significantly accelerating Monte Carlo simulations for complex systems like the 2D Ising model, with adaptable features for broader applications.

## Contribution

It introduces a GPU-accelerated, optimized version of population annealing with advanced features like automatic temperature adaptation and multi-histogram analysis.

## Key findings

- Speed-ups of several orders of magnitude over CPU implementations
- Effective adaptation of temperature steps for improved sampling
- Versatility for different spin models, including disordered systems

## Abstract

Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures.