Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study
arxiv(2023)
摘要
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.
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