Assessing Energy Consumption and Runtime Efficiency of Master- Worker Parallel Evolutionary Algorithms in CPU-GPU Systems

• Escobar, Juan José ^[1] ; Ortega, Julio ^[1] ; Díaz, Antonio ^[1] ; González, Jesús ^[1] ; Damas, Miguel ^[1]
  1. [1] Universidad de Granada

     Universidad de Granada

     Granada, España

     
• Localización: Annals of Multicore and GPU Programming: AMGP, ISSN 2341-3158, Vol. 4, Nº. 1, 2017, págs. 23-36
• Idioma: inglés
• Enlaces
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• Resumen

  • Thanks to parallel processing, it is possible not only to reduce code runtime but also energy consumption once the workload has been adequately distributed among the available cores. The current availability of heterogeneous architectures including GPU and CPU cores with different power-performance characteristics and mechanisms for dynamic voltage and frequency scaling does, in fact, pose a new challenge for developing efficient parallel codes that take into account both the achieved speedup and the energy consumed. This paper analyses the energy consumption and runtime behavior of a parallel master-worker evolutionary algorithm according to the workload distribution between GPU and CPU cores and their operation frequencies. It also proposes a model that has been fitted using multiple linear regression and which enables a workload distribution that considers both runtime and energy consumption by means of a cost function that suitably weights both objectives. Since many useful bioinformatics and data mining applications are tackled by programs with a similar profile to that of the parallel master-worker procedure considered here, the proposed energy-aware approach could be applied in many different situations.

• Referencias bibliográficas
  • Mittal, S.; Vetter, J.S.:”A survey of CPU-GPU heterogeneous computing techniques”. ACM Comput. Surv. 47, , Article 69, 35 pages. July, 2015....
  • O’Brien, K.; Pietri, I.; Reddy, R; Lastovetsky, A.; Sakellariou, R.:”A survey of power and energy models in HPC systems and applications”....
  • Lee, Y.C.; Zomaya, A.Y.:”Energy conious scheduling for distributed computing systems under different operationg conditions”. IEEE Trans. On...
  • Ortega, J.; Asensio-Cubero, J.; Gan, J. Q.; Ortiz, A.: “Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective...
  • GNU gprof manual: http://sourceware.org/binutils/docs/gprof/index.html
  • Escobar, J.J.; Ortega, J.; González, J.; Damas, M.; Díaz, A.F.: “Parallel high-dimensional multi-objective feature selection for EEG classification...
  • Weaver, V.N.; Johnson, M.; Kasichayanula, K.; Ralph, J.; Luszczek, P.; Terpstra, D.; Moore, S.:”Measuring energy and power with PAPI”. 41st...
  • Advanced configuration and power interface specification (ACPI): http://www.acpi.info/
  • CPU frequency scaling: https://wiki.archlinux.org/index.php/CPU_frequency_scaling
  • CPUFreq Governors: https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt
  • cpufreq.h: https://code.woboq.org/linux/linux/include/linux/cpufreq.h.html
  • Barik, R..; Farooqui, N.; Lewis, B.T.; Hu, C.; Shpeisman T.: “A black-box approach to energy-aware scheduling on integrated CPU-GPU systems”....
  • Hong, S.; Kim, H.:”An Integrated GPU Power and Performance Model”. SIGARCH Computer Architecture News. 2010;38(3):280–289.
  • Ge, R.; Feng, X.; Burtscher, M.; Zong, Z.: “PEACH: A Model for Performance and Energy Aware Cooperative Hybrid Computing”. In: CF’2014:24:1–...
  • Aliaga, J.I.; Barreda, M.; Dolz, M.F.; Martín, A.F.; Mayo, R.; Quintana-Ortí, E.S.:”Assessing the impact of the CPU power-saving modes on...
  • De Sensi, D.:”Predicting performance and power consumption of parallel applications”. In 24th Euromicro International Conference on Parallel,...
  • Dorronsoro, B.; Nesmachnow, S.; Taheri, J.; Zomaya, A.Y.; Talbi, E-G; Bouvry, P.:”A hierarchical approach for energy-efficient scheduling...
  • Ge, R.; Feng, X.; Cameron, K.W.:”Improvement of Power-Performance Efficiency for High-End Computing”.In: IPDPS’2005:233–240IEEE Computer Society;...
  • Wang, Y.; Ranganathan, N.:”An instruction-level energy estimation and optimization methodology for GPU”. h Intl. Conf. on Computer and Information...
  • Cebrián, J.M.; Guerrero, G.D.; García, J.M.:”Energy efficiency analysis of GPUs”. 2012 IEEE 26th Intl. Parallel and Distributed Processing...
  • Mittal, S.; Vetter, J.S.:”A survey of methods for analyzing and improving GPU energy efficiency”. ACM Comput. Surv. 47, 2, Article 19, 23...
  • Marowka, A.. “Energy Consumption Modeling for Hybrid Computing”. In: Euro-Par’2012:54–64 Springer; Rhodes Island, Greece.
  • Allen, T.; Ge, R..: “Characterizing Power and Performance of GPU Memory Access”. In: E2SC’2016:46– IEEE Press; 2016; Salt Lake City, Utah,...