Run-time mapping algorithm for dynamic workloads using process merging transformations

Exploration of task mappings has an important role to achieve high performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms. The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such systems. In this paper, a run-time algorithm is proposed to analytically evaluate the system throughput of to-be-executed applications (modelled as Kahn Process Networks, KPNs) in order to quickly determine a proper resource binding for these applications. Merging transformations on the KPNs are applied to capture the cases in which the number of processes in the KPN is larger than the number of available processing resources, thereby modeling the effects of binding multiple processes to a single processor. We evaluated our algorithm using a heterogeneous MPSoC system with several applications. Our experimental results revealed that during runtime, the performance of selected mapping with regard to available resources is close to the optimal performance obtained by exhaustive search and simulation. Therefore, the results clearly confirm that our algorithm is effective.