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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 todoi:10.1109/hpec.2013.6670333 dblp:conf/hpec/BomanW13 fatcat:5gfxh4alerhvvpe6vgf5tofuem