Shai Fine, Yishay Mansour
Machine Learning
In this paper, we present several algorithms for performing all-to-many personalized communication on distributed memory parallel machines. We assume that each processor sends a different message (of potentially different size) to a subset of all the processors involved in the collective communication. The algorithms are based on decomposing the communication matrix into a set of partial permutations. We study the effectiveness of our algorithms from both the view of static scheduling and runtime scheduling. © 1995 Academic Press, Inc.
Shai Fine, Yishay Mansour
Machine Learning
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