A Survey of Power and Energy Predictive Models in HPC Systems and Applications

Abstract

Power and energy efficiency are now critical concerns in extreme-scale high-performance scientific computing. Many extreme-scale computing systems today (for example: Top500) have tight integration of multicore CPU processors and accelerators (mix of Graphical Processing Units, Intel Xeon Phis, or Field Programmable Gate Arrays) empowering them to provide not just unprecedented computational power but also to address these concerns. However, such integration renders these systems highly heterogeneous and hierarchical, thereby necessitating design of novel performance, power, and energy models to accurately capture these inherent characteristics. There are now several extensive research efforts focusing exclusively on power and energy efficiency models and techniques for the processors composing these extreme-scale computing systems. This article synthesizes these research efforts with absolute concentration on predictive power and energy models and prime emphasis on node architecture. Through this survey, we also intend to highlight the shortcomings of these models to correctly and comprehensively predict the power and energy consumptions by taking into account the hierarchical and heterogeneous nature of these tightly integrated high-performance computing systems.

How to Measure Energy Consumption in Machine Learning Algorithms

“…The scope of this survey includes papers analyzed by [16], and [23]. We plan to extend this survey to include more estimation approaches, as presented in a recent work [26], where the authors portray significantly more estimation techniques. We have classified the papers based on three categories: type, technique, and level.…”

A Comparative Study of Methods for Measurement of Energy of Computing

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Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

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