GPU-accelerated Path-based Timing Analysis
Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years have seen many parallel PBA algorithms, but most of them are architecturally constrained by the CPU parallelism and do not scale beyond a few...
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| Vydané v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 721 - 726 |
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| Hlavní autori: | , , , |
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05.12.2021
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| Abstract | Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years have seen many parallel PBA algorithms, but most of them are architecturally constrained by the CPU parallelism and do not scale beyond a few threads. To overcome this challenge, we propose in this paper a new fast and accurate PBA algorithm by harnessing the power of graphics processing unit (GPU). We introduce GPU-efficient data structures, high-performance kernels, and efficient CPU-GPU task decomposition strateiges, to accelerate PBA to a new performance milestone. Experimental results show that our method can speed up the state-of-the-art algorithm by 543\times on a design of 1.6 million gates with exact accuracy. At the extreme, our method of 1 CPU and 1 GPU outperforms the state-of-the-art algorithm of 40 CPUs by 25-45\times. |
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| AbstractList | Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years have seen many parallel PBA algorithms, but most of them are architecturally constrained by the CPU parallelism and do not scale beyond a few threads. To overcome this challenge, we propose in this paper a new fast and accurate PBA algorithm by harnessing the power of graphics processing unit (GPU). We introduce GPU-efficient data structures, high-performance kernels, and efficient CPU-GPU task decomposition strateiges, to accelerate PBA to a new performance milestone. Experimental results show that our method can speed up the state-of-the-art algorithm by 543\times on a design of 1.6 million gates with exact accuracy. At the extreme, our method of 1 CPU and 1 GPU outperforms the state-of-the-art algorithm of 40 CPUs by 25-45\times. |
| Author | Huang, Tsung-Wei Guo, Guannan Wong, Martin Lin, Yibo |
| Author_xml | – sequence: 1 givenname: Guannan surname: Guo fullname: Guo, Guannan organization: University of Illinois at Urbana-Champaign,Department of Electrical and Computer Engineering,IL,USA – sequence: 2 givenname: Tsung-Wei surname: Huang fullname: Huang, Tsung-Wei organization: University of Utah,Department of Electrical and Computer Engineering,Salt Lake City,UT,USA – sequence: 3 givenname: Yibo surname: Lin fullname: Lin, Yibo organization: Peking University,Department of Computer Science,Beijing,China – sequence: 4 givenname: Martin surname: Wong fullname: Wong, Martin organization: University of Illinois at Urbana-Champaign,Department of Electrical and Computer Engineering,IL,USA |
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| Snippet | Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years... |
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| StartPage | 721 |
| SubjectTerms | Data structures Graphics processing units Instruction sets Logic gates Parallel processing Runtime Timing |
| Title | GPU-accelerated Path-based Timing Analysis |
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