Exposing Inter-process Information for Efficient PDES of Spatial Stochastic Systems on Multicores

We present a new approach for efficient process synchronization in parallel discrete event simulation on multicore computers. We aim specifically at simulation of spatially extended stochastic system models where time intervals between successive inter-process events are highly variable and without lower bounds: This includes models governed by the mesoscopic Reaction-Diffusion Master Equation (RDME). A central part of our approach is a mechanism for optimism control, in which each process disseminates accurate information about timestamps of its future outgoing interprocess events to its neighbours. This information gives each process a precise basis for deciding when to pause local processing to reduce the risk of expensive rollbacks caused by future “delayed” incoming events. We apply our approach to a natural parallelization of the Next Subvolume Method (NSM) for simulating systems obeying RDME. Since this natural parallelization does not expose accurate timestamps of future interprocess events, we restructure it to expose such information, resulting in a simulation algorithm called Refined Parallel NSM (Refined PNSM). We have implemented Refined PNSM in a parallel simulator for spatial extended Markovian processes. On 32 cores, it achieves an efficiency ranging between 43--95% for large models, and on average 37% for small models, compared to an efficient sequential simulation without any code for parallelization. It is shown that the gain of restructuring the naive parallelization into Refined PNSM more than outweighs its overhead. We also show that our resulting simulator is superior in performance to existing simulators on multicores for comparable models.