IEEE Transactions on Parallel and Distributed Systems
Parallel image restoration using surrogate constraint methods
Journal of Parallel and Distributed Computing
Heuristics for scheduling file-sharing tasks on heterogeneous systems with distributed repositories
Journal of Parallel and Distributed Computing
Parallel Computing - Algorithmic skeletons
Parallel multilevel algorithms for hypergraph partitioning
Journal of Parallel and Distributed Computing
Multi-level direct K-way hypergraph partitioning with multiple constraints and fixed vertices
Journal of Parallel and Distributed Computing
A Parallel Matrix Scaling Algorithm
High Performance Computing for Computational Science - VECPAR 2008
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Efficient successor retrieval operations for aggregate query processing on clustered road networks
Information Sciences: an International Journal
A Matrix Partitioning Interface to PaToH in MATLAB
Parallel Computing
On Two-Dimensional Sparse Matrix Partitioning: Models, Methods, and a Recipe
SIAM Journal on Scientific Computing
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Hypergraph partitioning for faster parallel pagerank computation
EPEW'05/WS-FM'05 Proceedings of the 2005 international conference on European Performance Engineering, and Web Services and Formal Methods, international conference on Formal Techniques for Computer Systems and Business Processes
On partitioning problems with complex objectives
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Partitioning Hypergraphs in Scientific Computing Applications through Vertex Separators on Graphs
SIAM Journal on Scientific Computing
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This paper addresses the problem of one-dimensional partitioning of structurally unsymmetric square and rectangular sparse matrices for parallel matrix-vector and matrix-transpose-vector multiplies. The objective is to minimize the communication cost while maintaining the balance on computational loads of processors. Most of the existing partitioning models consider only the total message volume hoping that minimizing this communication-cost metric is likely to reduce other metrics. However, the total message latency (start-up time) may be more important than the total message volume. Furthermore, the maximum message volume and latency handled by a single processor are also important metrics. We propose a two-phase approach that encapsulates all these four communication-cost metrics. The objective in the first phase is to minimize the total message volume while maintaining the computational-load balance. The objective in the second phase is to encapsulate the remaining three communication-cost metrics. We propose communication-hypergraph and partitioning models for the second phase. We then present several methods for partitioning communication hypergraphs. Experiments on a wide range of test matrices show that the proposed approach yields very effective partitioning results. A parallel implementation on a PC cluster verifies that the theoretical improvements shown by partitioning results hold in practice.