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Erschienen in:
Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Energy-Efficient Data Processing Through Data Sparsing with Artifacts

verfasst von : Pablo Graubner, Patrick Heckmann, Bernd Freisleben

Erschienen in: High Performance Computing

Verlag: Springer International Publishing

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Abstract

Improving the energy efficiency of software running in a data center is a challenging task. Several application-specific techniques, such as energy-aware heuristics, controlled approximation and energy-conserving I/O, have been proposed to tackle this problem. In this paper, we introduce data sparsing with artifacts, a novel approach to increase the energy efficiency of applications that are robust to input variations, such as speech and image processing. Data sparsing with artifacts is aimed at reducing the processing times and thus the energy efficiency of such applications while preserving the quality of the results by replacing a random subset of the original data with application-specific artifacts. In contrast to previous work, the proposed approach introduces artifacts at the data layer, without application layer modifications and with general purpose hardware. Data sparsing with artifacts has been integrated into a prototypical file system in userspace (FUSE) and the Hadoop Distributed File System (HDFS). Experiments with MapReduce-based face detection, face recognition and speech recognition algorithms show promising energy savings of up to 10 % with moderate accuracy losses for different data sparsing rates and artifacts.

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Vorheriges Kapitel Hop: Elastic Consistency for Exascale Data Stores
Nächstes Kapitel Updating the Energy Model for Future Exascale Systems
Fußnoten
8
Data sparsing might decrease the energy consumption of micro-controllers as well, due to a reduced transmission energy, but this is beyond the scope of this paper.
 
Literatur
1.
Agarwal, A., Rinard, M., Sidiroglou, S., Misailovic, S., Hoffmann, H.: Using Code Perforation to Improve Performance, Reduce Energy Consumption, and Respond to Failures. Technical report, MIT Dspace (2009)
2.
3.
Baek, W., Chilimbi, T.M.: Green: a framework for supporting energy-conscious programming using controlled approximation. In: Proceedings of the 2010 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2010, pp. 198–209. ACM (2010)
4.
Barroso, L.A., Clidaras, J., Hölzle, U.: The aatacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Archit. 8(3), 1–154 (2013)CrossRef
5.
Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. IEEE Comput. 40(12), 33–37 (2007)CrossRef
6.
7.
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 245–250. ACM (2001)
8.
9.
Chen, Y., Ganapathi, A., Katz, R.H.: To compress or not to compress - compute vs. i/o tradeoffs for mapreduce energy efficiency. In: Proceedings of the First ACM SIGCOMM Workshop on Green Networking, pp. 23–28. ACM (2010)
10.
Dasgupta, S., Gupta, A.: An elementary proof of a theorem of johnson and lindenstrauss. In: Random Structures & Algorithms, vol. 22, pp. 60–65. Wiley Subscription Services (2003)
11.
12.
13.
Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.: Architecture support for disciplined approximate programming. In: 17th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 301–312. ACM (2012)
14.
Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.: Neural acceleration for general-purpose approximate programs. In: Commun. ACM, vol. 58, pp. 105–115. ACM (2014)
15.
16.
17.
Kaushik, R.T., Bhandarkar, M.: GreenHDFS: Towards an energy-conserving, storage-efficient, hybrid hadoop compute cluster. In: 2010 International Conference on Power Aware Computing and Systems, pp. 1–9. USENIX Association (2010)
18.
Li, P., Hastie, T.J., Church, K.W.: Very sparse random projections. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 287–296. ACM (2006)
19.
Nair, R.: Big Data needs approximate computing: technical perspective. Commun. ACM 58, 104–104 (2014)CrossRef
20.
21.
Sampson, A., Dietl, W., Fortuna, E., Gnanapragasam, D., Ceze, L., Grossman, D.: EnerJ: approximate data types for safe and general low-power computation. SIGPLAN Not. 46(6), 164–174 (2011)CrossRef
22.
23.
24.
25.
26.
Tong, J., Nagle, D., Rutenbar, R.: Reducing power by optimizing the necessary precision/range of floating-point arithmetic. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 8(3), 273–286 (2000)CrossRef
27.
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 586–591 (1991)
28.
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
29.
Metadaten
Titel
Energy-Efficient Data Processing Through Data Sparsing with Artifacts
verfasst von
Pablo Graubner
Patrick Heckmann
Bernd Freisleben
Copyright-Jahr
2015
Buch
High Performance Computing

Print ISBN: 978-3-319-20118-4

Electronic ISBN: 978-3-319-20119-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-20119-1_23

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