Introduction to algorithms
Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
Performance effects of irregular communication patterns on massively parallel multiprocessors
Journal of Parallel and Distributed Computing
Sparse matrix computations on parallel processor arrays
SIAM Journal on Scientific Computing
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Distributed memory matrix-vector multiplication and conjugate gradient algorithms
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Massively parallel methods for engineering and science problems
Communications of the ACM
Rectilinear partitioning of irregular data parallel computations
Journal of Parallel and Distributed Computing
Mapping molecular dynamics computations on to hypercubes
Parallel Computing
Data distributions for sparse matrix vector multiplication
Parallel Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Image-Space Decomposition Algorithms for Sort-First Parallel Volume Rendering of Unstructured Grids
The Journal of Supercomputing
Partitioning Rectangular and Structurally Unsymmetric Sparse Matrices for Parallel Processing
SIAM Journal on Scientific Computing
Graph partitioning models for parallel computing
Parallel Computing - Special issue on graph partioning and parallel computing
Distributed processing of very large datasets with DataCutter
Parallel Computing - Clusters and computational grids for scientific computing
A hypergraph-partitioning approach for coarse-grain decomposition
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Partitioning Unstructured Computational Graphs for Nonuniform and Adaptive Environments
IEEE Parallel & Distributed Technology: Systems & Technology
All-to-All Broadcast on Switch-Based Clusters of Workstations
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
A Fine-Grain Hypergraph Model for 2D Decomposition of Sparse Matrices
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Partitioning an Array onto a Mesh of Processors
PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
Decomposing Irregularly Sparse Matrices for Parallel Matrix-Vector Multiplication
IRREGULAR '96 Proceedings of the Third International Workshop on Parallel Algorithms for Irregularly Structured Problems
Graph Partitioning and Parallel Solvers: Has the Emperor No Clother? (Extended Abstract)
IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
Parallel Multilevel Algorithms for Multi-constraint Graph Partitioning (Distinguished Paper)
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
SIAM Journal on Scientific Computing
Fast optimal load balancing algorithms for 1D partitioning
Journal of Parallel and Distributed Computing
Hypergraph partitioning for automatic memory hierarchy management
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Partitioning Sparse Matrices for Parallel Preconditioned Iterative Methods
SIAM Journal on Scientific Computing
Multi-level direct K-way hypergraph partitioning with multiple constraints and fixed vertices
Journal of Parallel and Distributed Computing
Heuristics for a matrix symmetrization problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
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We consider two-dimensional partitioning of general sparse matrices for parallel sparse matrix-vector multiply operation. We present three hypergraph-partitioning-based methods, each having unique advantages. The first one treats the nonzeros of the matrix individually and hence produces fine-grain partitions. The other two produce coarser partitions, where one of them imposes a limit on the number of messages sent and received by a single processor, and the other trades that limit for a lower communication volume. We also present a thorough experimental evaluation of the proposed two-dimensional partitioning methods together with the hypergraph-based one-dimensional partitioning methods, using an extensive set of public domain matrices. Furthermore, for the users of these partitioning methods, we present a partitioning recipe that chooses one of the partitioning methods according to some matrix characteristics.