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An Empirical Evaluation of Work Stealing with Parallelism Feedback Kunal Agrawal Yuxiong He Charles E. Leiserson
 

Summary: An Empirical Evaluation of 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, USA
Abstract
A-STEAL is a provably good adaptive work-stealing
thread scheduler that provides parallelism feedback to a
multiprocessor job scheduler. A-STEAL uses a simple
multiplicative-increase, multiplicative-decrease algorithm
to provide continual parallelism feedback to the job sched-
uler in the form of processor requests. Although jobs sched-
uled by A-STEAL can be shown theoretically to complete in
near-optimal time asymptotically while utilizing at least a
constant fraction of the allotted processors, the constants in
the analysis leave it open on whether A-STEAL works well
in practice. This paper confirms with simulation studies that
A-STEAL performs well when scheduling adaptively paral-
lel work-stealing jobs on large-scale multiprocessors.
Our studies monitored the behavior of A-STEAL on a

  

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