We propose a two-phase model for solving the problem of task repartitioning under data replication with memory constraints. The hypergraph-partitioning-based model proposed for the first phase aims to minimize the total message volume that will be incurred due to the replication/migration of input data while maintaining balance on computational and receive-volume loads of processors. The network-flow-based model proposed for the second phase aims to minimize the maximum message volume handled by processors via utilizing the flexibility in assigning send-communication tasks to processors, which is introduced by data replication. The validity of our proposed model is verified on parallelization of a direct volume rendering algorithm.
@InProceedings{aykanat_et_al:DagSemProc.09061.2, author = {Aykanat, Cevdet and Okuyan, Erkan and Cambazoglu, B. Barla}, title = {{A Model for Task Repartioning under Data Replication}}, booktitle = {Combinatorial Scientific Computing}, pages = {1--3}, series = {Dagstuhl Seminar Proceedings (DagSemProc)}, ISSN = {1862-4405}, year = {2009}, volume = {9061}, editor = {Uwe Naumann and Olaf Schenk and Horst D. Simon and Sivan Toledo}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09061.2}, URN = {urn:nbn:de:0030-drops-20903}, doi = {10.4230/DagSemProc.09061.2}, annote = {Keywords: Task repartitioning, data replication, hypergraph partitioning with fixed vertices, assignment flow network} }
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