Parallelization of Gillespie algorithm based on binary words

TL;DR

This paper introduces a parallelized Gillespie algorithm using binary word representation, significantly enhancing simulation efficiency and enabling the study of non-well mixed chemical systems like the Frank model.

Contribution

It proposes a novel bitwise parallelization method for the Gillespie algorithm, improving computational performance and extending its applicability.

Findings

Significant increase in simulation speed

Successful simulation of non-well mixed systems

Application to prebiotic chemistry models

Abstract

We present an improvement of the Gillespie Exact Stochastic Simulation Algorithm, which leverages a bitwise representation of variables to perform independent simulations in parallel. We show that the subsequent gain in computational yield is significant, and it may allow to perform simulations of non-well mixed chemical systems. We illustrate this idea with simulations of Frank model, originally introduced to explain the emergence of homochirality in prebiotic systems.