Warp-STAR: High-performance, Differentiable GPU-Accelerated Static Timing Analysis through Warp-oriented Parallel Orchestration
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917369068126208 |
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| author | Huang, En-Ming Hung, Shih-Hao |
| author_facet | Huang, En-Ming Hung, Shih-Hao |
| contents | Static timing analysis (STA) is crucial for Electronic Design Automation (EDA) flows but remains a computational bottleneck. While existing GPU-based STA engines are faster than CPU, they suffer from inefficiencies, particularly intra-warp load imbalance caused by irregular circuit graphs. This paper introduces Warp-STAR, a novel GPU-accelerated STA engine that eliminates this imbalance by orchestrating parallel computations at the warp level. This approach achieves a 2.4X speedup over previous state-of-the-art (SoTA) GPU-based STA. When integrated into a timing-driven global placement framework, Warp-STAR delivers a 1.7X speedup over SoTA frameworks. The method also proves effective for differentiable gradient analysis with minimal overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28381 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Warp-STAR: High-performance, Differentiable GPU-Accelerated Static Timing Analysis through Warp-oriented Parallel Orchestration Huang, En-Ming Hung, Shih-Hao Distributed, Parallel, and Cluster Computing Static timing analysis (STA) is crucial for Electronic Design Automation (EDA) flows but remains a computational bottleneck. While existing GPU-based STA engines are faster than CPU, they suffer from inefficiencies, particularly intra-warp load imbalance caused by irregular circuit graphs. This paper introduces Warp-STAR, a novel GPU-accelerated STA engine that eliminates this imbalance by orchestrating parallel computations at the warp level. This approach achieves a 2.4X speedup over previous state-of-the-art (SoTA) GPU-based STA. When integrated into a timing-driven global placement framework, Warp-STAR delivers a 1.7X speedup over SoTA frameworks. The method also proves effective for differentiable gradient analysis with minimal overhead. |
| title | Warp-STAR: High-performance, Differentiable GPU-Accelerated Static Timing Analysis through Warp-oriented Parallel Orchestration |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2603.28381 |