Using MAST for modeling and response-time analysis of real-time applications with GPUs
Iosu Gomez, Unai Díaz de Cerio, Jorge Parra, Juan M. Rivas, J. Javier Gutiérrez, Michael González Harbour · Journal of Systems Architecture · 2024
The ever increasing computing demands in embedded systems is driving the adoption of hardware accelerators such as GPUs , which offer powerful platforms that can compute parallel workloads efficiently. Relevant critical applications that benefit from such platforms, for instance autonomous driving , usually impose additional real-time requirements that must be met to guarantee the correctness of the systems. In this paper, we propose exploiting readily available and extensively validated techniques to model and analyze real-time systems with GPUs . Specifically, we propose a methodology to employ the MAST model to characterize such systems, and different variants of the Offset-Based Response-Time Analysis techniques to validate the real-time requirements. We verify our approach with a real industrial application sourced from the railway industry . Through a comprehensive evaluation involving synthetic and real task-sets, we characterize the applicability of the approach, and we also show how estimated worst-case response times are aligned with real measurements up to 87.2%.