HARD: Hardening Real-Time Scheduling and Analysis for Accelerator Enabled Computing

Yinchen Ni, Tianrui Ma, Jintao Chen, Chongye Yang, Siwei Ye, Yuankai Xu, Yier Jin, An Zou

Abstract: Despite the advancements in supporting artificial intelligence, accelerator-enabled computing architectures still struggle to meet strict timing constraints due to the complex interactions between CPU cores and accelerators. Although various scheduling and response-time analysis techniques have been developed, a significant gap remains between the conservative hard real-time schedulability (i.e., worst-case response times) and the average measured schedulability on real systems. This pessimism significantly limits the deployment of hard real-time tasks on accelerator-enabled computing platforms. To address this, we propose HARD, a real-time scheduling approach that integrates scheduling strategies, response time analysis, and practical scheduler designs for general accelerator-enabled computing platforms. Benefiting the subtask level segmented characteristics that are ignored by classic schedulers, the proposed HARD can significantly improve the theoretically guaranteed hard real-time schedulability. Extensive experiments on off-the-shelf Intel CPUs and NVIDIA GPUs show that HARD outperforms state-of-the-art scheduling and analysis approaches, delivering a 11.3% improvement in hard real-time schedulability and a remarkable 45.1 % reduction in pessimism.

External IDs:dblp:conf/rtas/NiMCYYXJZ25