A Fine-grained Approach for Power Consumption Analysis and Prediction - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

A Fine-grained Approach for Power Consumption Analysis and Prediction


Power consumption has became a critical concern in modern computing systems for various reasons including financial savings and environmental protection. With battery powered de-vices, we need to care about the available amount of energy since it is limited. For the case of supercomputers, as they imply a large aggregation of heavy CPU activities, we are exposed to a risk of overheating. As the design of current and future hardware is becoming more and more complex, energy prediction or estimation is as elusive as that of time performance. However, having a good prediction of power consumption is still an important request to the computer science community. Indeed, power consumption might become a common performance and cost metric in the near future. A good methodology for energy prediction could have a great impact on power-aware programming, compilation, or runtime monitoring. In this paper, we try to understand from measurements where and how power is consumed at the level of a computing node. We focus on a set of basic programming instructions, more precisely those related to CPU and memory. We propose an analytical prediction model based on the hypothesis that each basic instruction has an average energy cost that can be estimated on a given architecture through a series of micro-benchmarks. The considered energy cost per operation includes both the overhead of the embedding loop and associated (hardware/software) optimizations. Using these precalculated values, we derive a linear extrapolation model to predict the energy of a given algorithm expressed by means of atomic instructions. We then use three selected appli-cations to check the accuracy of our prediction method by comparing our estimations with the corresponding measurements obtained using a multimeter. We show a 9.48% energy prediction on sorting.

Dates and versions

hal-01074959 , version 1 (16-10-2014)



Alessandro Leite, Claude Tadonki, Christine Eisenbeis, Alba de Melo. A Fine-grained Approach for Power Consumption Analysis and Prediction. International Conference on Computational Science, ICCS'2014, Jun 2014, Cairns, Australia. ⟨10.1016/j.procs.2014.05.211⟩. ⟨hal-01074959⟩
242 View
0 Download



Gmail Facebook Twitter LinkedIn More