期刊
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
卷 41, 期 4, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2699470
关键词
Algorithms; Performance; Parallel; sparse; GPU; matrix-matrix
Sparse matrix-matrix multiplication (SpGEMM) is a key operation in numerous areas from information to the physical sciences. Implementing SpGEMM efficiently on throughput-oriented processors, such as the graphics processing unit (GPU), requires the programmer to expose substantial fine-grained parallelism while conserving the limited off-chip memory bandwidth. Balancing these concerns, we decompose the SpGEMM operation into three highly parallel phases: expansion, sorting, and contraction, and introduce a set of complementary bandwidth-saving performance optimizations. Our implementation is fully general and our optimization strategy adaptively processes the SpGEMM workload row-wise to substantially improve performance by decreasing the work complexity and utilizing the memory hierarchy more effectively.
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