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Ranking commercial machines through data transposition

Beau Piccart, Andy Georges UGent, Hendrik Blockeel and Lieven Eeckhout UGent (2011) International Symposium on Workload Characterization Proceedings. p.3-14
abstract
The performance numbers reported by benchmarking consortia and corporations provide little or no insight into the performance of applications of interest that are not part of the benchmark suite. This paper describes data transposition, a novel methodology for addressing this ubiquitous benchmarking problem. Data transposition predicts the performance for an application of interest on a target machine based on its performance similarities with the industry-standard benchmarks on a limited number of predictive machines. The key idea of data transposition is to exploit machine similarity rather than workload similarity as done in prior work, i.e., data transposition identifies a predictive machine that is most similar to the target machine of interest for predicting performance for the application of interest. We demonstrate the accuracy and effectiveness of data transposition using the SPEC CPU2006 benchmarks and a set of 117 commercial machines. We report that the machine ranking obtained through data transposition correlates well with the machine ranking obtained using measured performance numbers (average correlation coefficient of 0.93). Not only does data transposition improve average correlation, we also demonstrate that data transposition is more robust towards outlier benchmarks, i.e., the worst-case correlation coefficient improves from 0.59 by prior art to 0.71. More concretely, using data transposition to predict the top-1 machine for an application of interest leads to the best performing machine for most workloads (average deficiency of 1.2% and max deficiency of 24.8% for one benchmark), whereas prior work leads to deficiencies over 100% for some workloads.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
computer architecture, performance prediction, workload characterization
in
International Symposium on Workload Characterization Proceedings
issue title
2011 IEEE International symposium on workload characterization (IISWC)
pages
3 - 14
publisher
IEEE
place of publication
New York, NY, USA
conference name
2011 IEEE International symposium on Workload Characterization (IISWC 2011)
conference location
Austi, TX, USA
conference start
2011-11-06
conference end
2011-11-08
Web of Science type
Proceedings Paper
Web of Science id
000299350700001
ISBN
9781457720642
9781457720635
9781457720628
DOI
10.1109/IISWC.2011.6114192
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1977509
handle
http://hdl.handle.net/1854/LU-1977509
date created
2012-01-03 17:25:58
date last changed
2013-06-21 12:59:27
@inproceedings{1977509,
  abstract     = {The performance numbers reported by benchmarking consortia and corporations provide little or no insight into the performance of applications of interest that are not part of the benchmark suite. This paper describes data transposition, a novel methodology for addressing this ubiquitous benchmarking problem. Data transposition predicts the performance for an application of interest on a target machine based on its performance similarities with the industry-standard benchmarks on a limited number of predictive machines. The key idea of data transposition is to exploit machine similarity rather than workload similarity as done in prior work, i.e., data transposition identifies a predictive machine that is most similar to the target machine of interest for predicting performance for the application of interest.
We demonstrate the accuracy and effectiveness of data transposition using the SPEC CPU2006 benchmarks and a set of 117 commercial machines. We report that the machine ranking obtained through data transposition correlates well with the machine ranking obtained using measured performance numbers (average correlation coefficient of 0.93). Not only does data transposition improve average correlation, we also demonstrate that data transposition is more robust towards outlier benchmarks, i.e., the worst-case correlation coefficient improves from 0.59 by prior art to 0.71. More concretely, using data transposition to predict the top-1 machine for an application of interest leads to the best performing machine for most workloads (average deficiency of 1.2\% and max deficiency of 24.8\% for one benchmark), whereas prior work leads to deficiencies over 100\% for some workloads.},
  author       = {Piccart, Beau and Georges, Andy and Blockeel, Hendrik and Eeckhout, Lieven},
  booktitle    = {International Symposium on Workload Characterization Proceedings},
  isbn         = {9781457720642},
  keyword      = {computer architecture,performance prediction,workload characterization},
  language     = {eng},
  location     = {Austi, TX, USA},
  pages        = {3--14},
  publisher    = {IEEE},
  title        = {Ranking commercial machines through data transposition},
  url          = {http://dx.doi.org/10.1109/IISWC.2011.6114192},
  year         = {2011},
}

Chicago
Piccart, Beau, Andy Georges, Hendrik Blockeel, and Lieven Eeckhout. 2011. “Ranking Commercial Machines Through Data Transposition.” In International Symposium on Workload Characterization Proceedings, 3–14. New York, NY, USA: IEEE.
APA
Piccart, B., Georges, A., Blockeel, H., & Eeckhout, L. (2011). Ranking commercial machines through data transposition. International Symposium on Workload Characterization Proceedings (pp. 3–14). Presented at the 2011 IEEE International symposium on Workload Characterization (IISWC 2011), New York, NY, USA: IEEE.
Vancouver
1.
Piccart B, Georges A, Blockeel H, Eeckhout L. Ranking commercial machines through data transposition. International Symposium on Workload Characterization Proceedings. New York, NY, USA: IEEE; 2011. p. 3–14.
MLA
Piccart, Beau, Andy Georges, Hendrik Blockeel, et al. “Ranking Commercial Machines Through Data Transposition.” International Symposium on Workload Characterization Proceedings. New York, NY, USA: IEEE, 2011. 3–14. Print.