The Impact of Asynchronous GPGPU Behaviors on Stochastic Simulation

Abstract

This work examines the effect of one key aspect of General Purpose Graphics Processing Unit (GPGPU) computing on the realism and fidelity of stochastic simulations. In particular it is shown that the asynchronous nature of GPGPU computing can be leveraged to produce increased fidelity and realism, compared to conventional computing methods, when applied to probabilistic or stochastic simulations. This is a multifaceted argument that shows: 1) Asynchronous behaviors are essential to produce high computational throughput on GPGPU devices, and thus allow more rigorous sampling, which in turn enables a deeper understanding of the underlying stochastic processes. 2) Asynchronous GPGPU computing can eliminate the “global clock” present in simulations and potentially produce a better representation of the underlying process. This paper also attempts to give a working introduction to GPGPU computing, and to the applications of this technology in the field of stochastic simulation. A range of literature regarding these simulations is also surveyed, in order to provide context. A demonstration of synchronous versus asynchronous algorithms for robot swarm path planning is used to illustrate this discussion. Several notes on the limitations of GPGPU computing in this field are also made, along with remarks regarding future development of GPGPU-accelerated stochastic simulations.