The performance impact of granularity control and functional parallelism

Abstract

Task granularity and functional parallelism are fundamental issues in the optimization of parallel programs. Appropriate granularity for exploitation of parallelism is affected by characteristics of both the program and the execution environment. In this paper we demonstrate the efficacy of dynamic granularity control. The scheme we propose uses dynamic runtime information to select the task size of exploited parallelism at various stages of the execution of a program. We also demonstrate that functional parallelism can be an important factor in improving the performance of parallel programs, both in the presence and absence of loop-level parallelism. Functional parallelism can increase the amount of large-grain parallelism as well as provide finer-grain parallelism that leads to better load balance. Analytical models and benchmark results quantify the impact of granularity control and functional parallelism. The underlying implementation for this research is a low-overhead threads model based on user-level scheduling.