Automated level-based clustering of dataflow actors for controlled scheduling complexity

Dataflow Models of Computation (MoCs) significantly enhance parallel computing by efficiently expressing application parallelism on multicore architectures, unlocking greater performance and throughput. However, the complexity of graphs within dataflow-based systems can result in a time-consuming resource allocation process. To address this issue, a solution is to cluster computations to ease heuristic solving. The information encompassing the context of computations and the constraints of the architecture plays a crucial role in determining application performance. This paper presents an automated approach that leverages this information to control graph complexity prior to the resource allocation process. Experiments demonstrate that the proposed method, driven by clustering, not only yields improved throughput but also provides better mapping decisions and data transfer efficiency, achieving a throughput up to 1.8 times higher than state-of-the-art techniques.