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The performance evaluation in HPC, understanding the computational requirements of scientific applications, its relation with power consumption is a fundamental task to overcome the current barriers and to achieve the computational exascale. However, this imposes some challenging tasks, such as to monitor a wide range of parameters in heterogeneous environments, to enable fine grained profiling and power consumed across different components, to be language independent and to avoid code instrumentation. Considering these challenges, this work proposes the SMCis, an application monitoring tool developed with the goal of to collect all these aspects in an effective and accurate way, as well as to correlate these data graphically, with the environment of analysis and visualization.

This work aims to analyze the aspects related to the performance, without losing focus on the energy efficiency of the applications. To this end, we evaluated a representative set of experiments with three renowned benchmarks and two real world applications used by the energy industry. These experiments used a range of environments that included a medium scale HPC system with Xeon and CUDA cores and a mobile based Jetson TX2 development board composed of ARMv8 and CUDA cores. Our results enable analysing the performance and power consumption of the selected applications, and help to energy efficiecy in HPC systems.