working in- progress: NVIDIA GPU在虚拟化环境中的调度细节

Nicola Capodieci, R. Cavicchioli, M. Bertogna
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引用次数: 3

摘要

现代汽车级嵌入式平台具有高性能图形处理单元(gpu),以支持下一代自动驾驶应用所需的大规模并行处理能力。因此,需要一种具有强实时性保证的GPU调度方法。虽然以前的研究工作集中在GPU生态系统的逆向工程上,以理解和控制NVIDIA平台上的GPU调度,但我们深入解释了NVIDIA在Drive PX平台上对GPU应用程序调度的标准方法。然后,我们讨论如何使用特权调度服务器在虚拟化环境中执行任意调度策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Work-in-Progress: NVIDIA GPU Scheduling Details in Virtualized Environments
Modern automotive grade embedded platforms feature high performance Graphics Processing Units (GPUs) to support the massively parallel processing power needed for next-generation autonomous driving applications. Hence, a GPU scheduling approach with strong Real-Time guarantees is needed. While previous research efforts focused on reverse engineering the GPU ecosystem in order to understand and control GPU scheduling on NVIDIA platforms, we provide an in depth explanation of the NVIDIA standard approach to GPU application scheduling on a Drive PX platform. Then, we discuss how a privileged scheduling server can be used to enforce arbitrary scheduling policies in a virtualized environment.
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