Sparse matrix-matrix multiplication is a key kernel that has applications in several domains
such as scientific computing and graph analysis. Several algorithms have been studied in
the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse
matrix-matrix multiplication with a focus on performance portability across different high
performance computing architectures. The performance of these algorithms depend on the
data structures used in them. We compare different types of accumulators in these …