Scheduling and Response-time Analysis of Multicore and Multi-GPU Heterogeneous Systems with Server-Based Approach

This paper presents an advanced scheduling approach tailored for real-time tasks executing on heterogeneous computing platforms common in modern Networked Control Systems and Cyber-Physical Systems, which encompass both multi-core processors and multiple identical GPUs. Building on the server- based scheduling approach for single-GPU systems, our approach extends these principles to multi-GPU environments, addressing the complexities associated with managing multiple shared GPU resources. Introducing a dedicated server task to handle GPU requests in a priority-driven manner avoids the limitations of busy waiting and long priority inversions often encountered in traditional lock-based scheduling approaches. The paper also develops the worst-case response-time analysis of tasks that are scheduled using the proposed scheduling approach on these systems. The evaluation results demonstrate that our approach achieves comparable performance to the single-GPU server-based approach while delivering significant performance improvements over the traditional lock-based approaches, especially in improving the schedulability rate of task sets in multi-core and multi-GPU systems. Compared to existing synchronization-based approaches, the proposed framework avoids busy-waiting and reduces priority inversion while enabling analyzable GPU access in multi-GPU systems. In addition, the proposed response-time analysis explicitly captures GPU blocking and interference.