Optimizing Parallel Sparse Matrix-Vector Multiplication by Partitioning

Sparse matrix times vector multiplication is an important kernel in scientific computing. We study how to optimize the performance of this operation in parallel by reducing communication. We review existing approaches and present a new partitioning method for symmetric matrices. Our method is simple and can be implemented using existing software for hypergraph partitioning. Experimental results show our method produces better quality than traditional 1-dimensional partitioning methods and is competitive with 2-dimensional methods. It is also fast. Finally, we propose a graph model for an ordering problem to further optimize our approach.