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Cluster Computing
Erschienen in: Cluster Computing 1/2019

23.02.2018

Whole procedure heterogeneous multiprocessors low-power optimization at algorithm-level

verfasst von: Zhuowei Wang, Naixue Xiong, Hao Wang, Lianglun Cheng, Wuqing Zhao

Erschienen in: Cluster Computing | Sonderheft 1/2019

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Abstract

Power consumption reduction is the primary problem for the design and implementation of heterogeneous parallel systems. As it is difficult to make progress in the low-power optimization in the hardware layer to meet the increasing need for power optimization, more attention has been paid to low-power optimization in the hardware layer. The relationship between the execution time and dynamic power consumption of programs divided between homogeneous and heterogeneous computing sections is analysed. In addition, the communication power consumption for data transmission and dynamic multi-task allocation are described. Afterwards, this study establishes a power model for the whole procedure of heterogeneous parallel systems. By using this model, a selection algorithm is designed for the optimal frequency of processors with optimal power consumption under time constraints, optimal descent-based time allocation algorithms in multiple computing sections, and profiling dynamic analysis-based integral linear programming at algorithm-level, separately. Finally, the validity of the power optimization algorithm is ascertained using typical applications.
Vorheriger Artikel A novel disk I/O scheduling framework of virtualized storage system
Nächster Artikel Score level multibiometrics fusion approach for healthcare

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Literatur
1.
Holmbacka, S., Keller, J., Eitschberger, P., Lilius, J.: Accurate energy modelling for many-core static schedules. In: Euromicro International Conference on Parallel Distributed and Network-Based Processing, Turku, pp. 525–532 (2015)
2.
Zhao, Y., Li, X., Ju, L., Zong, Z.: Dependency-based energy-efficient scheduling for homogeneous muli-core clusters. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, 1299–1306 (2013)
3.
Jayaseelan, R.: Application-Specific Thermal Management of Computer Systems. National University of Singapore, Singapore (2014)
4.
Coskun, A.K.: Efficient Thermal Management for Multiprocessor Systems. University of California, San Diego (2014)
5.
Tabik, S., Villegas, A., Zapata, E.L., Romero, L.F.: Optimal tilt and orientation maps: a multi-algorithm approach for heterogeneous multicore-GPU system. J. Supercomput. 66(1), 135–147 (2013)CrossRef
6.
Qin, X., Mishra, P.: TECS: temperature-and energy-constrained scheduling for multicore systems. IEEE Trans. Parallel Distrib. Syst. 26(3), 868–877 (2015)CrossRef
7.
Sreraman, N., Govindarajan, A.: Vectorizing compiler for multimedia extensions. Int. J. Parallel Prog. 28(4), 363–400 (2012)CrossRef
8.
Andreas, K., Sylvain, L.: Compilation techniques for multimedia processors. Int. J. Parallel Prog. 28(4), 347–361 (2012)
9.
Rychly, M., Skoda, P., Smrz, P.: Heterogeneity-aware scheduler for stream processing frameworks. Int. J. Big Data Intell. 2(2), 70–80 (2015)CrossRef
10.
Mao, L., Qi, D.Y., Lin, W.W., et al.: An energy-efficient resource scheduling algorithm for cloud computing based on resource equivalence optimization. Int. J. Grid High Perform. Comput. 8(2), 43–57 (2016)CrossRef
11.
Tiwari, V., Malik, S., Wolfe, A., et al.: Instruction level power analysis and optimization of software. In: Proceedings of Ninth International Conference on VLSI Design, pp. 326–328 (1996)
12.
Chang, F., Farkas, K., Ranganathan, P.: Energy-driven statistical profiling: detecting software hotspots. In: Workshop on Power-Aware Computer Systems (2012)
13.
Landman, P.: High-level power estimation. In: Proceedings of the International Symposium on Low Power Electronics and Design, Piscataway, NJ, pp. 29–35 (1996)
14.
Brooks, D., Tiwari, V., Martonosi, M. Wattch: a framework for architectural-level power analysis and optimizations. In: Proceedings of the 27th Annual International Symposium on Computer Architecture, New York, pp. 83–94 (2010)
15.
Chen, J., Dubois, M., Stenstrom, P.: Integrating complete-system and user-level performance/power simulators: the SimWattch approach. In: IEEE International Symposium, In Performance Analysis of Systems and Software, pp. 1–10 (2013)
16.
Che, S., Boyer, M., Meng, J., et al. Rodinia: a benchmark suite for heterogeneous computing. In: Proceedings of 2013 IEEE International Symposium on Workload Characterization, pp. 44–54 (2013)
17.
18.
Luk, C.-K., Hong, S., Kim, H.: Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, New York, pp. 45–55 (2012)
19.
Yang, C., Wang, F., Du, Y., et al.: Adaptive optimization for petascale heterogeneous CPU/GPU Computing. In: Proceedings of IEEE International Conference on Cluster Computing, Los Alamitos, CA, pp. 19–28 (2012)
20.
Lorch, J.R.: Operating Systems Techniques for Reducing Processor Energy Consumption. University of California, Berkeley (2014)
21.
Weiser, M., Welch, B., Demers, A., Shenker, S.: Scheduling for reduced CPU energy. In: Proceedings of the 1st USENIX Conference on Operating Systems Design and Implementation. USENIX Association, Monterey, CA, vol. 353, pp. 13–23 (2014)
22.
Govil, K., Chan, E., Wasserman, H.: Comparing algorithms for dynamic speed-setting of a low-power CPU. In: Proceedings of the 11st Annual International Conference on Mobile Computing and Networking, Berkeley, CA, pp. 13–25 (2013)
23.
Lorch, J.R., Smith, A.J.: Improving dynamic voltage scaling algorithms with PACE. Joint International Conference on Measurement and Modeling of Computer Systems, Cambridge, MA 29(1), 50–61 (2014)
24.
Seng, J.S., Tullsen, D.M.: The effect of compiler optimizations on pentium 4 power consumption. In: Annual Workshop on Interaction between Compilers and Computer Architecture, pp. 51–56 (2013)
25.
Zhu, Y., Magklis, G., Scott, M.L.: The energy impact of aggressive loop fusion. In: Proceedings of the 13 the International Conference on Parallel Architectures and Compilation Techniques, Washington, DC, pp. 153–164 (2014)
26.
Kandemir, M., Vijaykrishnan, N., Irwin, M.: Influence of compiler optimizations on systems power. In: Proceedings of the 37th Annual Design Automation Conference, New York, pp. 304–307 (2013)
27.
Vidyarthi, D.P., Singh, S.K.: Independent tasks scheduling using parallel PSO in multiprocessor systems. Int. J. Grid High Perform. Comput. 7(2), 1–17 (2015)CrossRef
28.
Hsu, C.H., Slagter, K.D., Chen, S.C., et al.: Optimizing energy consumption with task consolidation in clouds. Inf. Sci. 258(3), 452–462 (2014)CrossRef
29.
Zong, Z., Manzanares, A., Ruan, X., Qin, X.: Ead and PEBE: two energy-aware duplication scheduling algorithms for parallel tasks on homogeneous clusters. IEEE Trans. Comput. 3, 360–374 (2011)CrossRefMATH
30.
La Fratta, P.A., Kogge, P.M.: Energy-efficient multithreading for a hierarchical heterogeneous multicore through locality-cognizant thread generation. Parallel Distrib. Comput. 73(12), 1551–1562 (2013)CrossRef
31.
Singh, J., Betha, S., Mangipudi, B., Auluck, N.: Contention aware energy efficient scheduling on heterogeneous multiprocessors. IEEE Trans. Parallel Distrib. Syst. 26(5), 1251–1264 (2015)CrossRef
32.
Chiesi, M., Vanzolini, L., Mucci, C.: Power-aware job scheduling on heterogeneous multicore architectures. IEEE Trans. Parallel Distrib. Syst. 26(3), 868–876 (2015)CrossRef
33.
Hunold, S., Rauber, T., Suter, F.: Redistribution aware two-step scheduling for mixed Cparallel applications. In: IEEE International Conference on Cluster Computing, pp. 50–58 (2013)
34.
Dutot, P.-F., Takpe, T., Suter, F.: Scheduling parallel task graphs on homogeneous multicluster platforms. IEEE Trans. Parallel Distrib. Syst. 20, 940–952 (2011)CrossRef
35.
Fu, Z.G., Sun, C.S., Luo, Z.Y.: A task scheduling algorithm of real-time leakage power and temperature optimization. In: Proceedings of the Computer Aided Design and Computer Graophics, Yellow Mountain City, China, pp. 484–491 (2013)
36.
Rai, D., Yang, H., Bacivarov, I., et al.: Worst-case temperature analysis for real-time systems. In: Proceedings of Design, Automation Test in Europe, Grenoble, France, pp. 631–636 (2011)
37.
Yang, C.Y., Thiele, L., Kuo, T.W.: Energy-efficient real-time task scheduling with temperature-dependent leakage. In: Proceedings of the Design, Automation Test in Europe, Dresden, Germany, pp. 9–14 (2010)
38.
Metadaten
Titel
Whole procedure heterogeneous multiprocessors low-power optimization at algorithm-level
verfasst von
Zhuowei Wang
Naixue Xiong
Hao Wang
Lianglun Cheng
Wuqing Zhao
Publikationsdatum
23.02.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1920-x

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