As side effects of the end of Dennard’s scaling, power and thermal technological walls stand in front of the evolution of supercomputers towards the exaflops era. Energy and temperature walls are big challenges to face for assuring a constant grow of performance in future. New generation architectures for HPC systems implement HW and SW components to address energy and thermal issues for increasing power and efficient computing in scientific workload. In thermal-bound HPC machines, workload-aware runtimes can leverage hardware knobs to guarantee the best operating point in term of performance and power saving without violating thermal constraints.In this paper, we present an integer-linear programming formulation for job mapping and frequency selection for thermal-bound HPC nodes. We use a fast solver and workload traces extracted from a real supercomputer to test our methodology. Our runtime is integrated into the MPI library, and it is capable of assigning high-performance cores to performance-critical processes. Critical processes are identified at execution time through a mathematical formulation, which relies on the characterization of the application workload and on the global synchronization barriers. We demonstrate that by combining long and short horizon predictions with information on the critical processes retrieved from the programming model, we can drastically improve the performance of the target application w.r.t. state-of-the-art DTM solutions.