Performance evaluation of Parallel Discrete-Event Simulation (PDES) environments is a complex task. PDES environments are commonly tested and compared using synthetic and real models. These models are built using environment-specific languages and are incompatible with other simulation platforms. There is a need for a common benchmark suite that can be used across different simulation kernels. This thesis suggests a framework for building such a benchmark suite. The framework centers around a workload specification language (WSL) --- a language for capturing the performance-critical characteristics of a workload. Therefore, a set of performance-critical parameters for workloads are identified and used to define the language. In addition to allowing the representation of actual simulation models, WSL can be used to define synthetic simulation models. Moreover, WSL has been designed to facilitate translation of the model to any specific simulation environment or language. A synthetic wor...

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 scalabilty 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.