Evolving Cut-Off Mechanisms and Other Work-Stealing Parameters for Parallel Programs

Optimizing parallel programs is a complex task because the interference among many different parameters. Work-stealing runtimes, used to dynamically balance load among different processor cores, are no exception. This work explores the automatic configuration of the following runtime parameters: dynamic granularity control algorithms, granularity control cache, work-stealing algorithm, lazy binary splitting parameter, the maximum queue size and the unparking interval. The performance of the program is highly sensible to the granularity control algorithm, which can be a combination of other granularity algorithms. In this work, we address two search-based problems: finding a globally efficient work-stealing configuration, and finding the best configuration just for an individual program. For both problems, we propose the use of a Genetic Algorithm (GA). The genotype of the GA is able to represent combinations of up to three cut-off algorithms, as well as other work-stealing parameters.