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- W3103168911 abstract "Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in numerous application domains such as data analytics, graph processing, and scientific computing. In this work we propose MatRaptor, a novel SpGEMM accelerator that is high performance and highly resource efficient. Unlike conventional methods using inner or outer product as the meta operation for matrix multiplication, our approach is based on row-wise product, which offers a better tradeoff in terms of data reuse and on-chip memory requirements, and achieves higher performance for large sparse matrices. We further propose a new hardware-friendly sparse storage format, which allows parallel compute engines to access the sparse data in a vectorized and streaming fashion, leading to high utilization of memory bandwidth. We prototype and simulate our accelerator architecture using gem5 on a diverse set of matrices. Our experiments show that MatRaptor achieves 129.2× speedup over single-threaded CPU, 8.8× speedup over GPU and 1.8× speedup over the state-of-the-art SpGEMM accelerator (OuterSPACE). MatRaptor also has 7.2× lower power consumption and 31.3× smaller area compared to OuterSPACE." @default.
- W3103168911 created "2020-11-23" @default.
- W3103168911 creator A5009386140 @default.
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- W3103168911 date "2020-10-01" @default.
- W3103168911 modified "2023-10-16" @default.
- W3103168911 title "MatRaptor: A Sparse-Sparse Matrix Multiplication Accelerator Based on Row-Wise Product" @default.
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- W3103168911 doi "https://doi.org/10.1109/micro50266.2020.00068" @default.
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