论文标题
优化的性能和内存footprintvia集成的CPU/GPU内存管理，自主驾驶平台上的动画
Co-Optimizing Performance and Memory FootprintVia Integrated CPU/GPU Memory Management, anImplementation on Autonomous Driving Platform
论文作者
论文摘要
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）依靠集成的CPU/GPU平台用于其DNNS驱动的工作负载，例如感知和其他高度平行的组件。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含义。通过一系列关于微基准和实际工作负载的实验，我们发现不同内存管理方法下的性能可能会根据应用程序特征而有所不同。基于此观察结果，我们开发了一个绩效模型，该模型可以根据应用程序特征预测每个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。通过执行每个任务内存管理策略切换和内核重叠，调度程序可以大大减轻系统内存压力并减少多任务处理共同运行的响应时间。我们使用Rodinia Benchmark Suite和两个真实的无人机软件和自动驾驶软件的现实案例研究，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。
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.
