A CUDA Implementation of the Spatial TAU-Leaping in Crowded Compartments (STAUCC) Simulator

The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.