Heterogeneous architectures characterize today hardware ranging from super-computers to smartphones. However, in spite of this importance, programming such systems is still challenging. In particular, it is challenging to map computations to the different processors of a heterogeneous device. In this paper, we provide a static analysis that mitigates this problem. Our contributions are two-fold: first, we provide a semi-context-sensitive algorithm, which analyzes the program's call graph to determine the best processor for each calling context. This algorithm is parameterized by a cost model, which takes into consideration processor's characteristics and data transfer time. Second, we show how to use simulated annealing to calibrate this cost model for a given heterogeneous architecture. We have used our ideas to build Etino, a tool that annotates C programs with OpenACC or OpenMP 4.0 directives. Etino generates code for a CPU-GPU architecture without user intervention. Experiments on classic benchmarks reveal speedups of up to 75x. Moreover, our calibration process lets \etino{} avoid slowdowns of up to 720x which trivial parallelization approaches would yield.