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arXiv · 2509.12207

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving

Hanqi Zhu·Wuyang Zhang·Xinran Zhang·Ziyang Tao·Xinrui Lin·Yu Zhang·Jianmin Ji·Yanyong Zhang
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Abstract

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.

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Hanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao, Xinrui Lin, Yu Zhang, Jianmin Ji, Yanyong Zhang. 2025-08-26. UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving. https://arxiv.org/abs/2509.12207

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