Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

The Coarse-Grained Monte Carlo (CGMC) method is a multi-scale stochastic mathematical and simulation framework for spatially distributed systems. CGMC simulations are important tools for studying phenomena such as catalysis, crystal growth, surface diffusion, phase transitions on single crystals, and cell membrane receptor dynamics. In parallel CGMC, the tau-leap method is used for parallel simulations that are executed on traditional CPU clusters in a master-slave setting. Unfortunately the communications between master and slaves negatively impact speedup and scalability. In this paper, we explore the potentials of GPUs for the tau-leap method and we present an extensive performance evaluation that leads to the most suitable degree of parallelism for this method under different simulation profiles. We show how the efficient parallelization of the tau-leap method for GPUs includes (1) the redefinition of its data structures, (2) the redesign of its algorithm, and (3) the selection of the most appropriate degree of parallelism (i.e., fine-grained or course-gained) on a single GPU or multiple GPUs. Exceptional performance improvements can thus be achieved for this method.