Cross Architectural Power Modelling

Existing power modelling research focuses on the model rather than the process for developing models. An automated power modelling process that can be deployed on different processors for developing power models with high accuracy is developed. For this, (i) an automated hardware performance counter selection method that selects counters best correlated to power on both ARM and Intel processors, (ii) a noise filter based on clustering that can reduce the mean error in power models, and (iii) a two stage power model that surmounts challenges in using existing power models across multiple architectures are proposed and developed. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual method reported in the literature, (ii) the noise filter reduces the mean error in power models by up to 55%, and (iii) the two stage power model can predict dynamic power with less than 8% error on both ARM and Intel processors, which is an improvement over classic models.

[1]  Bishop Brock,et al.  Accurate Fine-Grained Processor Power Proxies , 2012, 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture.

[2]  Sudarshan Raghunathan,et al.  Making a Supercomputer Do What You Want: High-Level Tools for Parallel Programming , 2006, Computing in Science & Engineering.

[3]  Sharad Malik,et al.  Instruction level power analysis and optimization of software , 1996, Proceedings of 9th International Conference on VLSI Design.

[4]  Li Shen,et al.  PPEP: Online Performance, Power, and Energy Prediction Framework and DVFS Space Exploration , 2014, 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture.

[5]  Israel Koren,et al.  A Study on the Use of Performance Counters to Estimate Power in Microprocessors , 2013, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  David M. Brooks,et al.  Energy characterization and instruction-level energy model of Intel's Xeon Phi processor , 2013, International Symposium on Low Power Electronics and Design (ISLPED).

[7]  Rachata Ausavarungnirun,et al.  SizeCap: Coordinating Energy Storage Sizing and Power Capping for Fuel Cell Powered Data Centers , 2016 .

[8]  Peter Arbenz,et al.  Introduction to parallel computing : a practical guide with examples in C , 2004 .

[9]  Jack J. Dongarra,et al.  A Scalable Cross-Platform Infrastructure for Application Performance Tuning Using Hardware Counters , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[10]  Peter Kilpatrick,et al.  Power Modelling for Heterogeneous Cloud-Edge Data Centers , 2017, PARCO.

[11]  Geoff V. Merrett,et al.  Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[12]  G. Marsaglia,et al.  The Ziggurat Method for Generating Random Variables , 2000 .

[13]  Leonie Kohl,et al.  Parallel Programming In C With Mpi And Open Mp , 2016 .

[14]  Paola Zuccolotto,et al.  Variable Selection Using Random Forests , 2006 .

[15]  José Augusto Baranauskas,et al.  How Many Trees in a Random Forest? , 2012, MLDM.

[16]  Waltenegus Dargie,et al.  A Stochastic Model for Estimating the Power Consumption of a Processor , 2015, IEEE Transactions on Computers.

[17]  Margaret Martonosi,et al.  Runtime Power Monitoring in High-End Processors: Methodology and Empirical Data , 2003, MICRO.

[18]  Eduard Ayguadé,et al.  A Systematic Methodology to Generate Decomposable and Responsive Power Models for CMPs , 2013, IEEE Transactions on Computers.

[19]  Christina Freytag,et al.  Using Mpi Portable Parallel Programming With The Message Passing Interface , 2016 .

[20]  Rajkumar Buyya,et al.  mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud , 2017, IEEE Transactions on Services Computing.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Osman S. Unsal,et al.  System-level power estimation tool for embedded processor based platforms , 2014, RAPIDO '14.

[23]  Peter Marwedel,et al.  An Accurate and Fine Grain Instruction-Level Energy Model Supporting Software Optimizations , 2007 .

[24]  Francisco J. Cazorla,et al.  Hardware support for accurate per-task energy metering in multicore systems , 2013, TACO.

[25]  Federico D. Sacerdoti,et al.  Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[26]  Xiao Zhang,et al.  Power containers: an OS facility for fine-grained power and energy management on multicore servers , 2013, ASPLOS '13.

[27]  Peter S. Pacheco.  Parallel programming with MPI , 1996 .