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A nested dissection partitioning method for parallel sparse matrix-vector multiplication
2013
2013 IEEE High Performance Extreme Computing Conference (HPEC)
We consider how to map sparse matrices across processes to reduce communication costs in parallel sparse matrixvector multiplication, an ubiquitous kernel in high performance computing. Our main contributions are: (i) an exact graph model for communication with general (two-dimensional) matrix distribution, and (ii) a recursive partitioning algorithm based on nested dissection that approximately solves this model. We have implemented our algorithm using hypergraph partitioning software to
doi:10.1109/hpec.2013.6670333
dblp:conf/hpec/BomanW13
fatcat:5gfxh4alerhvvpe6vgf5tofuem