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Austin, Texas
May 31, 1999 to June 4, 1999
ISBN: 0-7695-0228-8
pp: 0015
Prashanth B. Bhat , University of Southern California
Viktor K. Prasanna , University of Southern California
C.S. Raghavendra , The Aerospace Corporation
The Information Power Grid (IPG) is emerging as an infrastructure that will enable distributed applications -- such as video conferencing and distributed interactive simulation -- to seamlessly integrate collections of heterogeneous workstations, multiprocessors, and mobile nodes, over heterogeneous wide-area networks. This paper introduces a framework for developing efficient collective communication schedules in such systems. Our framework consists of analytical models of the heterogeneous system, scheduling algorithms for the collective communication pattern, and performance evaluation mechanisms. We show that previous models, which considered node heterogeneity but ignored network heterogeneity, can lead to solutions which are worse than the optimal by an unbounded factor. We then introduce an enhanced communication model, and develop three heuristic algorithms for the broadcast and multicast patterns. The completion time of the schedule is chosen as the performance metric. The heuristic algorithms are FEF (Fastest Edge First), ECEF (Earliest Completing Edge First), and ECEF with look-ahead. For small system sizes, we find the optimal solution using exhaustive search. Our simulation experiments indicate that the performance of our heuristic algorithms is close to optimal. For performance evaluation of larger systems, we have also developed a simple lower bound on the completion time. Our heuristic algorithms achieve significant performance improvements over previous approaches.
Information Power Grid, Collective Communication Algorithms, Broadcast, Multicast, Heterogeneous Networks.
Prashanth B. Bhat, Viktor K. Prasanna, C.S. Raghavendra, "Efficient Collective Communication in Distributed Heterogeneous Systems", ICDCS, 1999, 2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013 IEEE 33rd International Conference on Distributed Computing Systems 1999, pp. 0015, doi:10.1109/ICDCS.1999.776502
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