Cutting-edge embedded system applications, such as self-driving cars and unmanned drone software, are reliant on integrated CPU/GPU platforms for their DNNs-driven workload, such as perception and other highly parallel components. In this work, we set out to explore the hidden performance implication of GPU memory management methods of integrated CPU/GPU architecture. Through a series of experiments on micro-benchmarks and real-world workloads, we find that the performance under different memory management methods may vary according to application characteristics. Based on this observation, we develop a performance model that can predict system overhead for each memory management method based on application characteristics. Guided by the performance model, we further propose a runtime scheduler. By conducting per-task memory management policy switching and kernel overlapping, the scheduler can significantly relieve the system memory pressure and reduce the multitasking co-run response time. We have implemented and extensively evaluated our system prototype on the NVIDIA Jetson TX2, Drive PX2, and Xavier AGX platforms, using both Rodinia benchmark suite and two real-world case studies of drone software and autonomous driving software.

中文翻译：

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通过集成 CPU/GPU 内存管理协同优化性能和内存占用，在自动驾驶平台上实现

尖端的嵌入式系统应用程序，例如自动驾驶汽车和无人机软件，依赖于集成的 CPU/GPU 平台来处理其 DNN 驱动的工作负载，例如感知和其他高度并行的组件。在这项工作中，我们着手探索集成 CPU/GPU 架构的 GPU 内存管理方法的隐藏性能影响。通过对微基准测试和实际工作负载的一系列实验，我们发现不同内存管理方法下的性能可能会因应用程序特性而异。基于这一观察，我们开发了一个性能模型，可以根据应用程序特性预测每种内存管理方法的系统开销。在性能模型的指导下，我们进一步提出了一个运行时调度程序。通过进行每任务内存管理策略切换和内核重叠，调度器可以显着缓解系统内存压力，减少多任务协同运行响应时间。我们已经在 NVIDIA Jetson TX2、Drive PX2 和 Xavier AGX 平台上实施并广泛评估了我们的系统原型，使用 Rodinia 基准测试套件和无人机软件和自动驾驶软件的两个真实案例研究。