Lazy Scheduling: A Runtime Adaptive Scheduler for Declarative Parallelism

Lazy scheduling is a runtime scheduler for task-parallel codes that effectively coarsens parallelism on load conditions in order to significantly reduce its overheads compared to existing approaches, thus enabling the efficient execution of more fine-grained tasks. Unlike other adaptive dynamic schedulers, lazy scheduling does not maintain any additional state to infer system load and does not make irrevocable serialization decisions. These two features allow it to scale well and to provide excellent load balancing in practice but at a much lower overhead cost compared to work stealing, the golden standard of dynamic schedulers. We evaluate three variants of lazy scheduling on a set of benchmarks on three different platforms and find it to substantially outperform popular work stealing implementations on fine-grained codes. Furthermore, we show that the vast performance gap between manually coarsened and fully parallel code is greatly reduced by lazy scheduling, and that, with minimal static coarsening, lazy scheduling delivers performance very close to that of fully tuned code. The tedious manual coarsening required by the best existing work stealing schedulers and its damaging effect on performance portability have kept novice and general-purpose programmers from parallelizing their codes. Lazy scheduling offers the foundation for a declarative parallel programming methodology that should attract those programmers by minimizing the need for manual coarsening and by greatly enhancing the performance portability of parallel code.