Direct methods for sparse matrices
Direct methods for sparse matrices
A bridging model for parallel computation
Communications of the ACM
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Maximum matchings in sparse random graphs: Karp-Sipser revisited
Random Structures & Algorithms
BSPlib: The BSP programming library
Parallel Computing
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
A Fine-Grain Hypergraph Model for 2D Decomposition of Sparse Matrices
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
A framework for scalable greedy coloring on distributed-memory parallel computers
Journal of Parallel and Distributed Computing
Maximum matching in sparse random graphs
SFCS '81 Proceedings of the 22nd Annual Symposium on Foundations of Computer Science
Finding a Maximum Matching in a Sparse Random Graph in O(n) Expected Time
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Pregel: a system for large-scale graph processing - "ABSTRACT"
Proceedings of the 28th ACM symposium on Principles of distributed computing
Heuristic initialization for bipartite matching problems
Journal of Experimental Algorithmics (JEA)
A parallel approximation algorithm for the weighted maximum matching problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
A GPU algorithm for greedy graph matching
Facing the Multicore-Challenge II
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We present a parallel version of the Karp-Sipser graph matching heuristic for the maximum cardinality problem. It is bulk-synchronous, separating computation and communication, and uses an edge-based partitioning of the graph, translated from a two-dimensional partitioning of the corresponding adjacency matrix. It is shown that the communication volume of Karp-Sipser graph matching is proportional to that of parallel sparse matrix-vector multiplication (SpMV), so that efficient partitioners developed for SpMV can be used. The algorithm is presented using a small basic set of 7 message types, which are discussed in detail. Experimental results show that for most matrices, edge-based partitioning is superior to vertex-based partitioning, in terms of both parallel speedup and matching quality. Good speedups are obtained on up to 64 processors.