Arthur Nádas
IEEE Transactions on Neural Networks
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.
Arthur Nádas
IEEE Transactions on Neural Networks
Vicki L Hanson, Edward H Lichtenstein
Cognitive Psychology
Ryan Johnson, Ippokratis Pandis
CIDR 2013
Bemali Wickramanayake, Zhipeng He, et al.
Knowledge-Based Systems