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A Work-Efficient Parallel Sparse Matrix-Sparse Vector Multiplication Algorithm
2017
2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multiplication (SpMSpV) where the matrix, the input vector, and the output vector are all sparse. SpMSpV is an important primitive in the emerging GraphBLAS standard and is the workhorse of many graph algorithms including breadth-first search, bipartite graph matching, and maximal independent set. As thread counts increase, existing multithreaded SpMSpV algorithms can spend more time accessing the
doi:10.1109/ipdps.2017.76
dblp:conf/ipps/AzadB17
fatcat:g45fmpiw2fglnc3vofc62s7ggu