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Adaptive Work Stealing with Parallelism Feedback Kunal Agrawal Yuxiong He Charles E. Leiserson
 

Summary: Adaptive Work Stealing with Parallelism Feedback
Kunal Agrawal Yuxiong He Charles E. Leiserson
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
We present an adaptive work-stealing thread scheduler, A-
STEAL, for fork-join multithreaded jobs, like those written us-
ing the Cilk multithreaded language or the Hood work-stealing
library. The A-STEAL algorithm is appropriate for large paral-
lel 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 adapt its ex-
ecution to the processors allotted to it. Assuming that the job
scheduler never allots any job more processors than requested
by the job's thread scheduler, A-STEAL guarantees that the job
completes in near-optimal time while utilizing at least a con-
stant fraction of the allotted processors.

  

Source: Agrawal, Kunal - Department of Computer Science and Engineering, Washington University in St. Louis
Leiserson, Charles E. - Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)

 

Collections: Computer Technologies and Information Sciences