DagSemProc.09061.2.pdf
- Filesize: 94 kB
- 3 pages
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.
Feedback for Dagstuhl Publishing