Author retrospective for adaptive reduction parallelization techniques Conference Paper uri icon


  • © 2014 by the Association for Computing Machinery, Inc. (ACM). Modern applications are dynamic and input dependent and algorithm performance is input and environment sensitive. This potential mismatch between algorithmic choice and performance is exacerbated in the case of parallel programs because the penalty for less than optimal locality grows with the size of the machine. Reductions, e.g., map-reduce are one of the most important algorithms used in parallel codes are also input sensitive. This led us to develop an adaptive framework that used a statistical method to learn how to select the best algorithm for every execution instance. We applied it to parallel reduction algorithm selection. The importance of better reduction methods as well as adaptive selection methods has only increased since the time this paper was first published. Copyright

name of conference

  • 25th Anniversary International Conference

published proceedings

  • 25th Anniversary International Conference on Supercomputing Anniversary Volume -

author list (cited authors)

  • Yu, H., & Rauchwerger, L

citation count

  • 0

complete list of authors

  • Yu, Hao||Rauchwerger, Lawrence

editor list (cited editors)

  • Banerjee, U.

publication date

  • January 2014