Abstract
An efficient scheduler is important for task parallelism. It should provide scalable dynamic load-balancing mechanism among CPU cores. To meet this requirement, most runtime systems for task parallelism use work stealing as scheduling strategy. Work stealing schedulers typically steal work randomly. This strategy does not consider hardware specific knowledge such as memory hierarchy or application specific knowledge such as cache usage. In order to execute tasks more efficiently, work stealing schedulers should take such knowledge into account. To this end, we propose an API that can customize scheduling strategies and take hardware and application specific knowledge into account while preserving the desirable properties of work stealing.
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