A performance and scalability analysis framework for parallel discrete event simulators

Abstract

The development of efficient parallel discrete event simulators is hampered by the large number of interrelated factors affecting performance. This problem is made more difficult by the lack of scalable representative models that can be used to analyze optimizations and isolate bottlenecks. This paper proposes a performance and scalability analysis framework (PSAF) for parallel discrete event simulators. PSAF is built on a platform-independent Workload Specification Language (WSL). WSL is a language that represents simulation models using a set of fundamental performance-critical parameters. For each simulator under study, a WSL translator generates synthetic platform-specific simulation models that conform to the performance and scalability characteristics specified by the WSL description. Moreover, sets of portable simulation models that explore the effects of the different parameters, individually or collectively, on the execution performance can easily be constructed using the Synthetic Workload Generator (SWG). The SWG automatically generates simulation workloads with different performance properties. In addition, PSAF supports the seamless integration of real simulation models into the workload specification. Thus, a benchmark with both real and synthetically generated models can be built allowing for realistic and thorough exploration of the performance space. The utility of PSAF in determining the boundaries of performance and scalability of simulation environments and models is demonstrated.