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On Improving Sparse Matrix-Matrix Multiplication on GPUs

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2017, Master of Science, Ohio State University, Computer Science and Engineering.
General sparse matrix-matrix multiplication (SpGEMM) is an important primitive for many high perfomrance graph algorithms and algebraic multigrid solvers. Unlike the dense case, where performance of matrix-matrix multiplication is considerably higher than matrix-vector multiplication, the opposite is true for the sparse case on GPUs. A significant challenge is that the sparsity structure of the resulting sparse matrix is not known a priori, and the need to efficiently combine the additive contributions to its non-zero elements. We use synthetic matrices to characterize the effectiveness of alternate approaches and devise a hybrid approach that is demonstrated to be consistently superior to other available GPU SpMM implementations.
Sadayappan Ponnuswamy (Advisor)
Srinivasan Parthasarthy (Committee Member)
68 p.

Recommended Citations

Citations

  • Kunchum, R. (2017). On Improving Sparse Matrix-Matrix Multiplication on GPUs [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492694387445938

    APA Style (7th edition)

  • Kunchum, Rakshith. On Improving Sparse Matrix-Matrix Multiplication on GPUs. 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1492694387445938.

    MLA Style (8th edition)

  • Kunchum, Rakshith. "On Improving Sparse Matrix-Matrix Multiplication on GPUs." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492694387445938

    Chicago Manual of Style (17th edition)