无需修改代码，让自动驾驶的GPU任务自动优先执行。
UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
- 通过延迟启动和批量同步，透明调度GPU内核提升紧迫性响应。
- 实测减少61%的任务超时率，优于需改源码的现有方案。
- 适合自动驾驶系统开发者，尤其关注实时性能的团队。
自动驾驶技术快速发展，带来大量依赖GPU的实时任务，对车辆运行可靠性至关重要。然而，由于系统封闭及专有驱动限制，难以精细控制GPU执行，常导致任务超时，影响车辆性能。为此，我们提出UrgenGo——一种无需访问应用源码、非侵入式的紧急度感知GPU调度系统。该系统通过任务级流绑定、延迟内核启动和批量内核同步等机制，隐式提升关键任务的执行优先级。我们与一家自动驾驶初创公司合作，在一辆自动驾驶巴士上构建了11条真实的GPU任务链，并进行了全面实测。结果表明，相比需修改源码的先进调度器，UrgenGo将整体任务超时率降低了61%。
原文摘要 · Abstract (English)
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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