Model-Driven Simulation to Evaluate Performance Impact of Workload Features on Parallel Systems

Abstract

Parallel workloads in practice are far from being randomly distributed, instead they are highly repetitive because users tend to run the same applications over and over again. We refer to this phenomenon as temporal locality. In addition, the workloads exhibit a correlation between runtime and parallelism (i.e., number of processors) as is analysed in this paper. According to our best knowledge, there are very few studies on the impacts of these features on the performance of parallel systems. Since these impacts are not well known, researchers often evaluate scheduling algorithms with random workloads, which neglect the phenomenon of temporal locality and the correlation. This can result in an inaccurate scheduling evaluation for parallel systems, because our study shows that these two features can significantly affect scheduling performance. In our simulation-based experiments, an increase of the correlation can quickly degrade the parallel system performance and can change the result of comparing different scheduling policies. With respect to temporal locality, we indicate that this feature does not always seriously affect schedulers of parallel systems. Instead in particular situations, it can help to improve scheduling performance. Furthermore, we also discuss in this paper the necessity of using workloads with these features in scheduling evaluation as well as how to utilize the features to enhance the performance of schedulers.