Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

Abstract

In this paper, we present a solution to the industrial challenge put forth by ARM in 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use Microarchitectural Denial-of-Service (DoS) attacks as aggressor workloads of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application. This results in significant deviations of the estimated trajectories from known ground truths, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. To address the challenge, we propose RT-Gang++, a partitioned real-time gang scheduling framework with last-level cache (LLC) and integrated GPU bandwidth throttling capabilities. By applying RT-Gang++, we are able to achieve desired level of performance of the AR-HUD application even in the presence of fully loaded aggressor tasks.