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Summary: The Data Locality of Work Stealing
Umut A. Acar
School of Computer Science
Carnegie Mellon University
umut@cs.cmu.edu
Guy E. Blelloch
School of Computer Science
Carnegie Mellon University
guyb@cs.cmu.edu
Robert D. Blumofe
Department of Computer Sciences
University of Texas at Austin
rdb@cs.utexas.edu
Abstract
This paper studies the data locality of the work-stealing scheduling
algorithm on hardware-controlled shared-memory machines. We
present lower and upper bounds on the number of cache misses
using work stealing, and introduce a locality-guided work-stealing
algorithm along with experimental validation.
As a lower bound, we show that there is a family of multi-
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