UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908540388507648 |
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| author | Zhu, Hanqi Zhang, Wuyang Zhang, Xinran Tao, Ziyang Lin, Xinrui Zhang, Yu Ji, Jianmin Zhang, Yanyong |
| author_facet | Zhu, Hanqi Zhang, Wuyang Zhang, Xinran Tao, Ziyang Lin, Xinrui Zhang, Yu Ji, Jianmin Zhang, Yanyong |
| contents | The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary nature of these autonomous systems and closed-source GPU drivers hinder fine-grained control over GPU executions, often resulting in missed deadlines that compromise vehicle performance. To address this, we present UrgenGo, a non-intrusive, urgency-aware GPU scheduling system that operates without access to application source code. UrgenGo implicitly prioritizes GPU executions through transparent kernel launch manipulation, employing task-level stream binding, delayed kernel launching, and batched kernel launch synchronization. We conducted extensive real-world evaluations in collaboration with a self-driving startup, developing 11 GPU-bound task chains for a realistic autonomous navigation application and implementing our system on a self-driving bus. Our results show a significant 61% reduction in the overall deadline miss ratio, compared to the state-of-the-art GPU scheduler that requires source code modifications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving Zhu, Hanqi Zhang, Wuyang Zhang, Xinran Tao, Ziyang Lin, Xinrui Zhang, Yu Ji, Jianmin Zhang, Yanyong Operating Systems Robotics The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary nature of these autonomous systems and closed-source GPU drivers hinder fine-grained control over GPU executions, often resulting in missed deadlines that compromise vehicle performance. To address this, we present UrgenGo, a non-intrusive, urgency-aware GPU scheduling system that operates without access to application source code. UrgenGo implicitly prioritizes GPU executions through transparent kernel launch manipulation, employing task-level stream binding, delayed kernel launching, and batched kernel launch synchronization. We conducted extensive real-world evaluations in collaboration with a self-driving startup, developing 11 GPU-bound task chains for a realistic autonomous navigation application and implementing our system on a self-driving bus. Our results show a significant 61% reduction in the overall deadline miss ratio, compared to the state-of-the-art GPU scheduler that requires source code modifications. |
| title | UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving |
| topic | Operating Systems Robotics |
| url | https://arxiv.org/abs/2509.12207 |