Heterogeneous parallel programming has two main problems on large computation systems: the first is the increase of power consumption on supercomputers in proportion to the number of computational resources used to obtain high performance, the second one is the underuse of these resources by scientific applications with improper distribution of tasks. Select the optimal computational resources and make a good mapping of task granularity is the fundamental challenge for the next generation of Exascale Systems. This research proposes an integrated energy-aware scheme called efficiently energetic acceleration (EEA) for large-scale scientific applications running on heterogeneous architectures. The EEA scheme uses statistical techniques to get GPU power levels to create a GPU power cost function and obtains the computational resource set that maximizes energy efficiency for a provided workload. The programmer or load balancing framework can use the computational resources obtained to schedule the map parallel task granularity in static time.