Co-Optimizing Performance and Memory FootprintVia Integrated CPU/GPU Memory Management, anImplementation on Autonomous Driving Platform

TL;DR

This paper investigates GPU memory management impacts on integrated CPU/GPU platforms in autonomous driving, developing a performance model and runtime scheduler to optimize performance and memory usage.

Contribution

It introduces a performance model and runtime scheduler for GPU memory management, enabling dynamic policy switching to improve system performance in embedded autonomous systems.

Findings

Memory management methods affect performance based on application characteristics.

The proposed scheduler reduces system memory pressure and multitasking response time.

System prototype evaluation shows significant performance improvements.

Abstract

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.