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Sparse matrix-matrix multiplication (SpGEMM) is a computational primitivethat is widely used in areas ranging from traditional numerical applications torecent big data analysis and machine learning. Although many SpGEMM algorithmshave been proposed, hardware specific optimizations for multi- and many-coreprocessors are lacking and a detailed analysis of their performance undervarious use cases and matrices is not available. We firstly identify andmitigate multiple bottlenecks with memory management and thread scheduling onIntel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- andmany-core processors, we develop a hash-table-based algorithm and optimize aheap-based shared-memory SpGEMM algorithm. We examine their performancetogether with other publicly available codes. Different from the literature,our evaluation also includes use cases that are representative of real graphalgorithms, such as multi-source breadth-first search or triangle counting. Ourhash-table and heap-based algorithms are showing significant speedups fromlibraries in the majority of the cases while different algorithms dominate theother scenarios with different matrix size, sparsity, compression factor andoperation type. We wrap up in-depth evaluation results and make a recipe togive the best SpGEMM algorithm for target scenario. A critical finding is thathash-table-based SpGEMM gets a significant performance boost if the nonzerosare not required to be sorted within each row of the output matrix.

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures