Toward a Fast Stochastic Simulation Processor for Biochemical Reaction Networks

Computational studies of biological systems have gained widespread attention as a promising alternative to regular experimentation. Within this domain, stochastic simulation algorithms are widely used for in-silico studies of biochemical reaction networks, such as gene regulatory networks. However, inherent computational complexities limit wide-spread adoption and make traditional software solutions on general-purpose computers prohibitively slow. In this paper, we present a specialized stochastic simulation processor that exploits fine-and coarse-grain parallelism in Gillepie's first reaction method to achieve high performance. The processor is designed to support large-scale networks more than a million species and reactions using external DRAMs. In addition, we introduce a dedicated compiler that creates data locality for efficient memory access and data reuse. Our performance evaluation using cycle-accurate simulation shows that our approach achieves orders of magnitude higher throughput for networks with different characteristics of coupling, compared to best-in-class software algorithms on a state-of-the-art workstation.