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• W2625440595 abstract "Matrix operations are common and expensive computations in a variety of applications. They occur frequently in high-performance computing, graphics, graph processing, and machine learning applications. This paper discusses how to map a variety of important matrix computations, including sparse matrix-vector multiplication (SpMV), sparse triangle solve (SpTS), graph processing, and dense matrix-matrix multiplication, to GPUs. Since many emerging systems will use heterogeneous architectures (e.g. CPUs and GPUs) to attain the desired performance targets under strict power constraints, this paper discusses implications and future research for matrix processing with heterogeneous designs." @default.
• W2625440595 created "2017-06-23" @default.
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• W2625440595 date "2017-07-01" @default.
• W2625440595 modified "2023-09-26" @default.
• W2625440595 title "Accelerating Matrix Processing with GPUs" @default.
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• W2625440595 doi "https://doi.org/10.1109/arith.2017.14" @default.
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