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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Yunquan Zhou, Huiyang Li, Shigang Yan, Shengen |
| Copyright Year | 2016 |
| Description | Author Affiliation: SenseTime Group Limited, Department of Information Engineering, Chinese University of Hong Kong(Department of electrical and computer engineering, North Carolina State University, Raleigh, NC (Zhou, Huiyang; Li, Shigang); State key laboratory of computer architecture, Institute of Computing Technologies, Chinese Academy of Sciences, Beijing, China (Zhang, Yunquan; Yan, Shengen)) |
| Abstract | Sparse Matrix-Vector multiplication (SpMV) is a key operation in engineering and scientific computing. Although the previous work has shown impressive progress in optimizing SpMV on many-core architectures, load imbalance and high memory bandwidth remain the critical performance bottlenecks. We present our novel solutions to these problems, for both GPUs and Intel MIC many-core architectures. First, we devise a new SpMV format, called Blocked Compressed Common Coordinate (BCCOO). BCCOO extends the blocked Common Coordinate (COO) by using bit flags to store the row indices to alleviate the bandwidth problem. We further improve this format by partitioning the matrix into vertical slices for better data locality. Then, to address the load imbalance problem, we propose a highly efficient matrix-based segmented sum/scan algorithm for SpMV, which eliminates global synchronization. At last, we introduce an autotuning framework to choose optimization parameters. Experimental results show that our proposed framework has a significant advantage over the existing SpMV libraries. In single precision, our proposed scheme outperforms clSpMV COCKTAIL format by 255% on average on AMD FirePro W8000, and outperforms CUSPARSE V7.0 by 73.7% on average and outperforms CSR5 by 53.6% on average on GeForce Titan X; in double precision, our proposed scheme outperforms CUSPARSE V7.0 by 34.0% on average and outperforms CSR5 by 16.2% on average on Tesla K20, and has equivalent performance compared with CSR5 on Intel MIC. |
| Starting Page | 1 |
| Ending Page | 25 |
| Page Count | 25 |
| File Format | |
| ISSN | 15443566 |
| e-ISSN | 15443973 |
| DOI | 10.1145/2994148 |
| Volume Number | 13 |
| Issue Number | 4 |
| Journal | ACM Transactions on Architecture and Code Optimization (TACO) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-25 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | BCCOO CUDA GPU Intel MIC OpenCL SpMV Parallel algorithms Segmented scan |
| Content Type | Text |
| Resource Type | Article |
| Subject | Hardware and Architecture Information Systems Software |
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