Accurate approximation and MPI parallelization of spatial stochastic reaction-diffusion in STEPS

Spatial stochastic reaction-diffusion simulations have become an important component of molecular modeling in Computational Neuroscience, as shown in a growing number of recent studies including our previous work in which we show that stochastic effects, in particular stochastic calcium dynamics, contribute to Purkinje cell calcium burst variability There are common challenges of parallelization for reaction-diffusion simulators as well as some unique challenges for STEPS, in which space is discretized into irregular tetrahedrons and not cubes that are commonly used by other reaction-diffusion simulators. Exact solutions are not beneficial, where stochastic diffusion across geometry partitions result in frequent conflict between nodes, requiring regular costly rollbacks. Therefore it is essential that an approximation algorithm is applied. Such an approximation must minimise loss of accuracy whilst maximising performance gain. Existing solutions such as the Gillespie-Multi-Particle method (GMP) Our method is implemented on an MPI framework, and we present performance gains under two scenariosglobal application of the method, and application only at partition boundaries. We discuss the potential of this method for future large-scale parallel simulations in STEPS