A Study and Analysis of Energy Efficiency Techniques in Heterogeneous Multi-Core Architectures
|International Journal of Computer Science and Engineering|
|© 2017 by SSRG - IJCSE Journal|
|Volume 4 Issue 6|
|Year of Publication : 2017|
|Authors : Y. ShebbirAli|
How to Cite?
Y. ShebbirAli, "A Study and Analysis of Energy Efficiency Techniques in Heterogeneous Multi-Core Architectures," SSRG International Journal of Computer Science and Engineering , vol. 4, no. 6, pp. 36-40, 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I6P107
Heterogeneous Multi-core architectures are using widely for improving energy aware without degrading efficiency of the system. Current many energy aware techniques are there in Heterogeneous Multi-core architectures but it is not reaching as much as user’sexpectations. Now our study is conducting by comparing the various techniques on Heterogeneous Multi-core architectures and how they are efficiency in fullfilling their existing needs of the Heterogeneous Multi-core architectures,here we are producing the Comparative result analysis and study on existing various energy aware techniques for the Heterogeneous Multi-core architectures.
Comparative result analysis and study on existing various energy aware techniques for the Heterogeneous Multi-core architectures.
 M. Showerman, J. Enos, A. Pant, V. Kindratenko, C. Steffen,R. Pennington, and W.-m. Hwu, ―QP: A heterogeneousmulti-accelerator cluster, in Proc. 10th LCI Int. Conf. HighPerform.ClusteredComput., 2009, pp. 1–8.
 V. V. Kindratenko, J. J. Enos, G. Shi, M. T. Showerman, G. W.Arnold, J. E. Stone, J. C. Phillips, and W. -M. Hwu, ―Gpuclustersfor high-performance computing, in Proc. IEEE Int. Conf.ClusterComput. Workshops, 2009, pp. 1–8.
 ORNL. (2012, Dec.).Titan project timeline.[Online].Available:http://www.olcf.ornl.gov/titan/
 Federal Energy Management Program, ―Quick start guide toincrease data center energy efficiency, U.S. Department ofEnergy, Tech. Rep., 2012. [Online]. Available: http://hightech.lbl.gov/documents/data_centers/Quick-Start- Guide.pdf
 C. Lefurgy, X. Wang, and M. Ware, ―Power capping: A prelude topower shifting, Cluster Comput., vol. 11, no. 2, pp. 183–195, 2008.
 X. Wang, M. Chen, C. Lefurgy, and T. W. Keller, ―SHIP: A scalablehierarchical power control architecture for large-scale data centers,IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 1, pp. 168–176,Jan. 2012.
 R. Ge, X. Feng, S. Song, H.-C. Chang, D. Li, and K. W. Cameron,―PowerPack: Energy profiling and analysis of highperformancesystems and applications, IEEE Trans. Parallel Distrib. Syst.,vol. 21, no. 5, pp. 658–671, May 2010.
 M. Y. Lim, V. W. Freeh, and D. K. Lowenthal, ―Adaptive, transparentfrequency and voltage scaling of communication phases inMPI programs, in Proc. ACM/IEEE Conf. Supercomput., 2006, p. 14.
 N. Kappiah, V. W. Freeh, and D. K. Lowenthal, ―Just in timedynamic voltage scaling: Exploiting inter-node slack to saveenergy in mpi programs, in Proc. ACM/IEEE Conf. Supercomput.,2005, p. 33.
 C.-H. Hsu and W.-C. Feng, ―A power-aware run-time system forhigh-performance computing, in Proc. ACM/IEEE Conf. Supercomput.,2005, p. 1.
 K. H. Kim, R. Buyya, and J. Kim, ―Power aware scheduling of bagof-tasksapplications with deadline constraints on DVS-enabledclusters, in Proc. 7th IEEE Int. Symp. Cluster Comput. Grid, 2007,pp. 541– 548.
 X. Wang and M. Chen, ―Cluster-level feedback power control forperformance optimization, in Proc. IEEE 14th Int. Symp.High Perform.Comput. Archit., 2008, pp. 101–110.
 M. Etinski, J. Corbalan, J. Labarta, and M. Valero, ―Parallel jobscheduling for power constrained HPC systems, Parallel Comput.,vol. 38, pp. 615–630, 2012.
 B. Lin, A. Mallik, P. Dinda, G. Memik, and R. Dick, ―Userandprocess- driven dynamic voltage and frequency scaling, in Proc.IEEE Int. Symp. Perform. Anal. Syst. Softw., 2009, pp. 11–22.
 W. L. Bircher and L. K. John, ―Core-level activity prediction formulticore power management, IEEE J. Emerging Select. Topics CircuitsSyst., vol. 1, no. 3, pp. 218–227, Sep. 2011.
 H. Ltaief, P. Luszczek, and J. Dongarra, ―Profiling high performancedense linear algebra algorithms on multicore architecturesfor power and energy efficiency, Comput. Sci.- Res.Develop.,vol. 27, no. 4, pp. 277–287, 2012.
 E. Anderson, LAPACK Users‘ Guide. SIAM, Philadelphia, PA,USA, vol. 9, 1999.
 PLASMA—Parallel linear algebra software for multicore architectures,Version 2.4.5, 2011.
 C. Lively, X. Wu, V. Taylor, S. Moore, H.-C. Chang, C.- Y.Su, andK.Cameron, ―Power-aware predictive models of hybrid (MPI/OpenMP) scientific applications on multicore systems, Comput.Sci.-Res. Develop., vol. 27, no. 4, pp. 245–253, 2012.
 Y. Hotta, M. Sato, H. Kimura, S. Matsuoka, T. Boku, and D.Takahashi, ―Profile-based optimization of power performanceby using dynamic voltage scaling on a PC cluster, in Proc.20th Int. Parallel Distrib.Process.Symp., 2006, pp. 298–298.
 W. Ye, N. Vijaykrishnan, M. Kandemir, and M. J. Irwin, ―Thedesign and use of simplepower: A cycle-accurate energy estimationtool, in Proc. 37th ACM Annu. Desi.Autom. Conf., 2000,pp. 340–345.
 D. Brooks, V. Tiwari, and M. Martonosi, ―Wattch: A frameworkfor architectural-level power analysis and optimizations, ACMSIGARCH Comput. Archit. News, vol. 28, no. 2, pp. 83–94, 2000.
 R. Suda and D. Q. Ren, ―Accurate measurements and precisemodeling of power dissipation of CUDA kernels toward poweroptimized high performance CPU-GPU computing, in Proc. Int.Conf.Parallel Distrib.Comput., Appl. Technol., 2009, pp. 432–438.
 X. Ma, M. Dong, L. Zhong, and Z. Deng, ―Statistical power consumptionanalysis and modeling for GPU-based computing, inProc. ACM SOSP Workshop Power Aware Comput. Syst., 2009,pp. 1–6.
 P. Bohrer, E. N. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, C.McDowell, and R. Rajamony, ―The case for power management inweb servers, in Power Aware Computing, New York, NY, USA:Springer-Verlag, 2002, pp. 261–289.
 ‗Nvidia CUDA Programming Guide, Nvidia, Santa Clara, CA, USA,2011.
 F. Ries, T. De Marco, and R. Guerrieri, ―Triangular matrix inversionon heterogeneous multicore systems, IEEE Trans. ParallelDistrib. Syst., vol. 23, no. 1, pp. 177–184, Jan. 2012.
 A. Krampe, J. Lepping, and W. Sieben, ―A hybrid Markov chainmodel for workload on parallel computers, in Proc. 19th ACMInt.Symp. High Perform. Distrib.Comput., 2010, pp. 589–596.
 N. Sharifimehr and S. Sadaoul, ―Markovian workload modelingfor enterprise application servers, in Proc. 2nd Canadian Conf.Comput. Sci. Softw. Eng., 2009, pp. 161– 168.
 J. Heo, P. Jayachandran, I. Shin, D. Wang, T. Abdelzaher, and X.Liu, ―OptiTuner: On performance composition and server farmenergy minimization application, IEEE Trans. Parallel Distrib.Syst., vol. 22, no. 11, pp. 1871–1878, Nov. 2011.