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    Scaling sparse matrix-matrix multiplication in the accumulo database
    (Springer, 2020) Demirci, Gündüz Vehbi; Aykanat, Cevdet
    We propose and implement a sparse matrix-matrix multiplication (SpGEMM) algorithm running on top of Accumulo’s iterator framework which enables high performance distributed parallelism. The proposed algorithm provides write-locality while ingesting the output matrix back to database via utilizing row-by-row parallel SpGEMM. The proposed solution also alleviates scanning of input matrices multiple times by making use of Accumulo’s batch scanning capability which is used for accessing multiple ranges of key-value pairs in parallel. Even though the use of batch-scanning introduces some latency overheads, these overheads are alleviated by the proposed solution and by using node-level parallelism structures. We also propose a matrix partitioning scheme which reduces the total communication volume and provides a balance of workload among servers. The results of extensive experiments performed on both real-world and synthetic sparse matrices show that the proposed algorithm scales significantly better than the outer-product parallel SpGEMM algorithm available in the Graphulo library. By applying the proposed matrix partitioning, the performance of the proposed algorithm is further improved considerably.