Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform
Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform
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DOI：
10.1109/rtas48715.2020.00007
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发表时间：
2020-03
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影响因子：
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通讯作者：
Soroush Bateni;Zhendong Wang;Yuankun Zhu;Yang Hu;Cong Liu
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文献类型：
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作者：
Soroush Bateni;Zhendong Wang;Yuankun Zhu;Yang Hu;Cong Liu
先进的嵌入式系统应用，如自动驾驶汽车和无人机软件，都依赖于集成的CPU/GPU平台来处理其DNN驱动的工作负载，如感知和其他高度并行的组件。在这项工作中，我们开始探索隐藏的性能暗示的GPU内存管理方法的集成CPU/GPU架构。通过对微基准测试和实际工作负载的一系列实验，我们发现不同的内存管理方法下的性能可能会根据应用程序的特点而有所不同。基于这一观察，我们开发了一个性能模型，可以预测系统开销为每一个内存管理方法的基础上应用程序的特点。在性能模型的指导下，我们进一步提出了一个运行时调度器。该调度器通过对每个任务进行内存管理策略切换和内核重叠，可以显著缓解系统内存压力，减少多任务协同运行的响应时间。我们已经在NVIDIA Jetson TX 2、Drive PX 2和Xavier AGX平台上实施并广泛评估了我们的系统原型，使用了Rodinia基准测试套件以及两个无人机软件和自动驾驶软件的真实案例研究。
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.