Data Dependency based Parallel Simulation of Wireless Networks

Abstract

Simulation of wireless systems is highly complex and can only be efficient if the simulation is executed in parallel. To this end, independent events have to be identified to enable their simultaneous execution. Hence, the number of events identified as independent needs to be maximized in order to increase the level of parallelism. Traditionally, dependencies are determined only by time and location of events: If two events take place on the same simulation entity, they must be simulated in timestamp order. Our approach to overcome this limitation is to also investigate data-dependencies between events. This enables event reordering and parallelization even for events at the same simulation entity. To this end, we design the simulation language PSimLa, which aids this process. In this paper, we discuss the PSimLa design and compiler as well as our data-dependency analysis approach in detail and present case studies of wireless network models, speeded up by a factor of 10 on 12 cores where time-based parallelization only achieves a 1.6x speedup.