Adaptive work stealing with parallelism feedback

Abstract

We present an adaptive work-stealing thread scheduler, A-Steal, for fork-join multithreaded jobs, like those written using the Cilk multithreaded language or the Hood work-stealing library. The A-Steal algorithm is appropriate for large parallel servers where many jobs share a common multiprocessor resource and in which the number of processors available to a particular job may vary during the job's execution. A-Steal provides continual parallelism feedback to a job scheduler in the form of processor requests, and the job must adaptits execution to the processors allotted to it. Assuming that the job scheduler never allots any job more processors than requested by thejob's thread scheduler, A-Steal guarantees that the job completes in near-optimal time while utilizing at least a constant fraction of the allotted processors.