Optimizing the deployment of real-time OpenMP applications for energy efficiency

Designing and deploying real-time computing pipelines efficiently on modern embedded platforms is increasingly challenging due to the growing complexity of hardware architectures, often featuring multi-core processors, frequency scaling capabilities, heterogeneous cores for enhanced power efficiency, and hardware accelerators. OpenMP is a prominent tool for parallelizing applications on multi-core platforms and is gaining increasing adoption in the domain of real-time systems. However, providing sound performance guarantees on the timing behavior of complex parallel computations organized as graph structures on heterogeneous platforms, while achieving optimal or near-optimal energy efficiency, is all but trivial. This paper tackles this problem by proposing a methodology to deploy and analyze both traditional parallel real-time applications and OpenMP parallel applications, modeled as directed acyclic graphs (DAGs) and coexisting on the same heterogeneous platform. Specifically, the approach targets asymmetric multi-core platforms with frequency scaling capabilities, with the aim of minimizing energy consumption while guaranteeing end-to-end latency constraints via schedulability analysis. The proposed approach features an optimal solver based on a mixed-integer quadratic constrained programming formulation, and a computationally efficient heuristic to extract high-quality solutions with reduced solving time. The concept is experimentally validated using randomly generated sets of DAGs, optimized by the two techniques and deployed using an OpenMP-based DAG synthetic benchmark on Linux running on an embedded board. Results demonstrate that the methodology enables energy-efficient deployment of mixed traditional and OpenMP real-time DAG applications while preserving end-to-end latency guarantees.