Optimization of population annealing Monte Carlo for large-scale spin-glass simulations

TL;DR

This paper enhances population annealing Monte Carlo, a parallel algorithm for simulating complex spin-glass systems, by optimizing implementation, algorithms, and scalability, enabling efficient large-scale simulations of various frustrated and higher-order Hamiltonians.

Contribution

It introduces multiple optimization strategies for population annealing Monte Carlo, improving its efficiency and scalability for large-scale spin-glass and frustrated systems.

Findings

Optimized implementation for better performance.

Enhanced parallelization for large-scale simulations.

Applicable to a variety of complex Hamiltonians.

Abstract

Population annealing Monte Carlo is an efficient sequential algorithm for simulating k-local Boolean Hamiltonians. Because of its structure, the algorithm is inherently parallel and therefore well suited for large-scale simulations of computationally hard problems. Here we present various ways of optimizing population annealing Monte Carlo using 2-local spin-glass Hamiltonians as a case study. We demonstrate how the algorithm can be optimized from an implementation, algorithmic accelerator, as well as scalable parallelization points of view. This makes population annealing Monte Carlo perfectly suited to study other frustrated problems such as pyrochlore lattices, constraint-satisfaction problems, as well as higher-order Hamiltonians commonly found in, e.g., topological color codes.