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The Sparse Matrix-Vector Multiplication (SpMV) kernel is used in a broad class of linear algebra computations. SpMV computations result in a performance bottleneck in many high performance applications, so optimizing… Click to show full abstract
The Sparse Matrix-Vector Multiplication (SpMV) kernel is used in a broad class of linear algebra computations. SpMV computations result in a performance bottleneck in many high performance applications, so optimizing SpMV performance is paramount. While implementing this kernel on a GPU can potentially boost performance significantly, current GPU libraries either provide modest performance gains or are burdened with high sparse format conversion overhead.
Keywords:
sparse format;
gpu memory;
efficient gpu;
vcsr efficient;
performance
Journal Title: IEEE Transactions on Parallel and Distributed Systems
Year Published: 2022
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Journal Title: IEEE Transactions on Parallel and Distributed Systems
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!