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Sparse Matrix-Matrix multiplication is a key kernel that has applications inseveral domains such as scientific computing and graph analysis. Severalalgorithms have been studied in the past for this foundational kernel. In thispaper, we develop parallel algorithms for sparse matrix-matrix multiplicationwith a focus on performance portability across different high performancecomputing architectures. The performance of these algorithms depend on the datastructures used in them. We compare different types of accumulators in thesealgorithms and demonstrate the performance difference between these datastructures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose theright algorithm and data structure based on the characteristics of the problem.We show performance comparisons on three architectures and demonstrate the needfor the community to develop two phase sparse matrix-matrix multiplicationimplementations for efficient reuse of the data structures involved.

Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures