Coarse-grained reconfigurable architectures (CGRA) are designed to deliver high-performance computing while drastically reducing the latency of the computing system. Although they are often highly domain-specifically optimized, they keep several levels of flexibility so that they can be reused. However, their reuse is generally limited due to the complexity of identifying the best allocation of new tasks into the hardware resources. Another limiting point is the complexity to produce a reliable performance analysis for each new implementation. To solve this problem, we propose to consider CGRA as a programmable, configuration-driven computing fabric, called Coarse-Grained Programmable Architecture (CGPA). We propose a new latency-based model to describe all hardware elements. We demonstrate how to implicitly model, with the help of latency's prediction, the heterogeneity of their material implementations. Our model provides the possibility to assess also the configuration cost, often neglected in other works. The design of the modelling framework allows it to become a part of a complete application mapping and scheduling chain, up to the automated generation of the execution context, thus maximizing the reusability of the given CGPA.