Real-Time Scheduling and Analysis of Fixed-Priority Tasks on a Basic Heterogeneous Architecture With Multiple CPUs and Many PEs

While accelerator-based heterogeneous architectures have gained traction in accelerating AI tasks, effectively managing them with stringent timing constraints remains a challenge. Although many scheduling and response time analysis approaches are proposed for multi-core or heterogeneous multi-core (i.e., big.LITTLE cores) processors, direct application of them to accelerator-based heterogeneous architectures with multiple CPUs and numerous processing units (PEs) often results in significant pessimism. This paper introduces real-time scheduling and comprehensive response time analysis from unit-level micro view to job-level macro view, for general accelerator-based heterogeneous architectures, greatly enhancing schedulability and utilization rates. We begin by establishing a general task execution pattern on heterogeneous architectures that integrates multiple CPU cores and various PEs. Subsequently, we present a real-time scheduling strategy and corresponding response time analysis based on this task execution pattern from micro to macro views. Through extensive experiments conducted on GEMM and AI workloads, our proposed scheduling and response time analysis significantly outperforms state-of-the-art scheduling algorithms, improving schedulability by 10.3% to 52.9%. Furthermore, experiments on NVIDIA GPU systems indicate a potential pessimism reduction of up to 30.7%. As we target general heterogeneous architectures, our approach can be readily applied to off-the-shelf accelerator-based heterogeneous computing systems, ensuring adherence to deadlines and enhancing schedulability.