Processor self-scheduling in parallel discrete event simulation

Abstract

This paper describes a novel data structure and an algorithm for processor self-scheduling in parallel discrete event simulation. The presented data structure allows the efficient scheduling of future computations, it facilitates the inexpensive use of processor affinity information, it reduces the contention on the scheduling queue, and it integrates load balancing and locality management methods into a single mechanism. We use the behavioral simulation of a multiprocessor system to characterize the behavior of the proposed data structure and the associated scheduling algorithm. The results of our study show that it is important to maintain as detailed affinity information as possible and exploit this information at run time.