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Performance–energy adaptation of parallel programs in pervasive computing

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Abstract

It is meaningful to use a little energy to obtain more performance improvement compared with the increased energy. It also makes sense to relax a small quantity of performance restriction to save an enormous amount of energy. Trading a small amount of energy for a considerable sum of performance or vice versa is possible if the relativities between performance and energy of parallel programs are exactly known. This work studies the relativities by recording the performance speedup and energy consumption of parallel programs when the number of cores on which programs run are changed. We demonstrate that the performance improvement and the increased energy consumption have a linear negative correlation.In addition, these relativities can guide us to do performance–energy adaptation under two assumptions. Our experiments show that the average correlation coefficients between performance and energy are higher than 97 %. Furthermore, it can be found that exchanging less than 6 % performance loss for more than 37 % energy consumption is feasible and vise versa.

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Notes

  1. The real speedup is higher than ideal speedup for some parallel programs at a few points. This is because the working set size of per-thread decreases when spawning more threads, thus performance gets improved due to the fact that the cache miss rate of per-thread reduces. The measured execution time is also affected by the precision of sniper simulator.

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Acknowledgments

This work was supported by National High-tech Research and Development Program of China (863 Program) under Grant No. 2012AA010905, and China National Natural Science Foundation under Grants Nos. 61272408, 61133006. Doctoral Fund of Ministry of Education of China under Grant No. 20130142110048.

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Authors and Affiliations

  1. Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Liang Zhu, Hai Jin & Xiaofei Liao

  2. Electrical and Computer Engineering, University of Maine, Maine, 04469, USA

    Jianhui Yue

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  1. Liang Zhu
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  2. Hai Jin
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  3. Xiaofei Liao
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  4. Jianhui Yue
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Correspondence to Hai Jin.

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Zhu, L., Jin, H., Liao, X. et al. Performance–energy adaptation of parallel programs in pervasive computing. J Supercomput 70, 1260–1278 (2014). https://doi.org/10.1007/s11227-014-1226-6

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  • DOI: https://doi.org/10.1007/s11227-014-1226-6

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