The growing demand for smarter high-performance embedded systems leads to the integration of multiple functionalities in on-chip systems with tens (even hundreds) of cores. This trend opens a very challenging question about the optimal resource allocation in those manycore systems. Answering this question is key to meet the performance and energy requirements. This paper deals with a learning technique applicable to manycore systems in order to predict mapping-related performances. The resulting prediction models can enable to improve dynamic resource allocation decisions. Our proposal is demonstrated on two automotive application case studies with very promising results.