Kadir Akbudak

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For outer-product–parallel sparse matrix-matrix multiplication (SpGEMM) of the form C=A×B, we propose three hypergraph models that achieve simultaneous partitioning of input and output matrices without any replication of input data. All three hypergraph models perform conformable one-dimensional (1D) columnwise and 1D rowwise partitioning of the input(More)
Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand(More)
The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand(More)
Sparse matrix-vector and matrix-transpose-vector multiplication (SpMM<sup>T</sup>V) repeatedly performed as z&#x2190;A<sup>T</sup><sub>x</sub> and y&#x2190; A z (or y A w) for the same sparse matrix A is a kernel operation widely used in various iterative solvers. One important optimization for serial SpMM<sup>T</sup>V is reusing A-matrix nonzeros, which(More)
Exploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form <inline-formula><tex-math notation="LaTeX">$C=A\,B$ </tex-math><alternatives><inline-graphic xlink:href="aykanat-ieq1-2656893.gif"/></alternatives></inline-formula> on many-core(More)
The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand(More)
The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand(More)
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