The evaluation of performance and power consumption is a key step in the design of applications for large computational systems as supercomputers and clusters (multicore and accelerator nodes, multicore and coprocessor nodes, manycore and accelerator nodes). In these systems the developers must design several experiments for workload characterization observing the architectural implications when using different combinations of computational resources such as number of GPU, number of cores for processing, number of cores for administration of GPU, number of MPI processes and thread affinity policy. It should also engage factors as the clock frequency and memory usage as well select the combination of computational resources that increases the performance and minimizes the power consumption. This research proposes an integrated energy-aware scheme called efficiently energetic acceleration (EEA) for large-scale scientific applications running on heterogeneous architectures. This paper shows the use of a monitoring tool with two components called enerGyPU and enerGyPhi to recording EEA control factors in runtime on two environments: one cluster with multicore and accelerator nodes (2-CPU/8-GPU) and one server with multiple cores and one coprocessor (2-CPU/1-MIC). These monitors allow to analyze multiple testing results under different parameter combinations to observe the EEA control factors that determine the energy efficiency.