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

Juan José Escobar, Julio Ortega, Antonio Díaz, Jesús González, Miguel Damas

Abstract


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.

Keywords


Energy-aware workload distribution; heterogeneous parallel architectures; master-worker parallel evolutionary algorithms

References


Mittal, S.; Vetter, J.S.:”A survey of CPU-GPU heterogeneous computing techniques”. ACM Comput. Surv. 47,

, Article 69, 35 pages. July, 2015. DOI: http://dx.doi.org/10.1145/2788396.

O’Brien, K.; Pietri, I.; Reddy, R; Lastovetsky, A.; Sakellariou, R.:”A survey of power and energy models in

HPC systems and applications”. ACM Comput. Surv. 50, 3, Article 37, 38 pages. July, 2017. DOI:

http://dx.doi.org/10.1145/3078811.

Lee, Y.C.; Zomaya, A.Y.:”Energy conious scheduling for distributed computing systems under different

operationg conditions”. IEEE Trans. On Parallel and Distributed Systems, Vol.22, No.8, pp.1374-1381.

August, 2011.

Ortega, J.; Asensio-Cubero, J.; Gan, J. Q.; Ortiz, A.: “Classification of motor imagery tasks for BCI with

multiresolution analysis and multiobjective feature selection”. BioMedical Engineering OnLine, 2016.

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 with dynamic workload balancing on CPU-GPU”. Cluster Computing.

;20(3):1881–1897.

Weaver, V.N.; Johnson, M.; Kasichayanula, K.; Ralph, J.; Luszczek, P.; Terpstra, D.; Moore, S.:”Measuring

energy and power with PAPI”. 41st Intl. Conference on Parallel Processing Workshops (ICPPW), pp. 262-268,

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”. In: CGO’2016:70–81ACM; 2016; Barcelona, Spain.

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– 24:2ACM; 2014; Cagliari, Italy.

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 the task-parallel solution of sparse linear systems”. Cluster Computing, 17, pp.

-1348, 2014.

De Sensi, D.:”Predicting performance and power consumption of parallel applications”. In 24th Euromicro

International Conference on Parallel, Distributed, and Network-Based Processing (PDP), 2016. DOI:

1109/PDP.2016.41.

Dorronsoro, B.; Nesmachnow, S.; Taheri, J.; Zomaya, A.Y.; Talbi, E-G; Bouvry, P.:”A hierarchical approach

for energy-efficient scheduling of large workloads in multicore distributed systems”. Sustainable Computing:

Informatics and Systems, 4, pp.252-261, 2014.

Ge, R.; Feng, X.; Cameron, K.W.:”Improvement of Power-Performance Efficiency for High-End Computing”.

In: IPDPS’2005:233–240IEEE Computer Society; 2005; Denver, Colorado, USA.

Wang, Y.; Ranganathan, N.:”An instruction-level energy estimation and optimization methodology for GPU”.

11th Intl. Conf. on Computer and Information Technology, pp.621-628, 2011.

Cebrián, J.M.; Guerrero, G.D.; García, J.M.:”Energy efficiency analysis of GPUs”. 2012 IEEE 26th Intl.

Parallel and Distributed Processing Symp. Workshops & PhD Forum, pp. 1014-1022, 2012.

Mittal, S.; Vetter, J.S.:”A survey of methods for analyzing and improving GPU energy efficiency”. ACM

Comput. Surv. 47, 2, Article 19, 23 pages. July, 2014. DOI: http://dx.doi.org/10.1145/2636342.

Marowka, A.. “Energy Consumption Modeling for Hybrid Computing”. In: Euro-Par’2012:54–64Springer;

; 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, USA.


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