Fast automatic heuristic construction using active learningCitation formats

Standard

Fast automatic heuristic construction using active learning. / Ogilvie, William F.; Petoumenos, Pavlos; Wang, Zheng; Leather, Hugh.

Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers. ed. / James Brodman; Peng Tu. Springer Nature, 2015. p. 146-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8967).

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Ogilvie, WF, Petoumenos, P, Wang, Z & Leather, H 2015, Fast automatic heuristic construction using active learning. in J Brodman & P Tu (eds), Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8967, Springer Nature, pp. 146-160, 27th International Workshop on Languages and Compilers for Parallel Computing , Hillsboro, United States, 15/09/14. https://doi.org/10.1007/978-3-319-17473-0_10

APA

Ogilvie, W. F., Petoumenos, P., Wang, Z., & Leather, H. (2015). Fast automatic heuristic construction using active learning. In J. Brodman, & P. Tu (Eds.), Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers (pp. 146-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8967). Springer Nature. https://doi.org/10.1007/978-3-319-17473-0_10

Vancouver

Ogilvie WF, Petoumenos P, Wang Z, Leather H. Fast automatic heuristic construction using active learning. In Brodman J, Tu P, editors, Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers. Springer Nature. 2015. p. 146-160. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-17473-0_10

Author

Ogilvie, William F. ; Petoumenos, Pavlos ; Wang, Zheng ; Leather, Hugh. / Fast automatic heuristic construction using active learning. Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers. editor / James Brodman ; Peng Tu. Springer Nature, 2015. pp. 146-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{82c598c78adf4076a2d0774d9a674421,
title = "Fast automatic heuristic construction using active learning",
abstract = "Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data. However, obtaining this data can take months per platform. This is becoming an ever more critical problem and if no solution is found we shall be left with out of date heuristics which cannot extract the best performance from modern machines. In this work, we present a low-cost predictive modelling approach for automatic heuristic construction which significantly reduces this training overhead. Typically in supervised learning the training instances are randomly selected to evaluate regardless of how much useful information they carry. This wastes effort on parts of the space that contribute little to the quality of the produced heuristic. Our approach, on the other hand, uses active learning to select and only focus on the most useful training examples. We demonstrate this technique by automatically constructing a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu–Gpu based heterogeneous system. Our methodology is remarkably simple and yet effective, making it a strong candidate for wide adoption. At high levels of classification accuracy the average learning speed-up is 3x, as compared to the state-of-the-art.",
keywords = "Machine learning, Workload scheduling",
author = "Ogilvie, {William F.} and Pavlos Petoumenos and Zheng Wang and Hugh Leather",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-17473-0_10",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "146--160",
editor = "James Brodman and Peng Tu",
booktitle = "Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers",
address = "United States",
note = "27th International Workshop on Languages and Compilers for Parallel Computing , LCPC 2014 ; Conference date: 15-09-2014 Through 17-09-2014",

}

RIS

TY - GEN

T1 - Fast automatic heuristic construction using active learning

AU - Ogilvie, William F.

AU - Petoumenos, Pavlos

AU - Wang, Zheng

AU - Leather, Hugh

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data. However, obtaining this data can take months per platform. This is becoming an ever more critical problem and if no solution is found we shall be left with out of date heuristics which cannot extract the best performance from modern machines. In this work, we present a low-cost predictive modelling approach for automatic heuristic construction which significantly reduces this training overhead. Typically in supervised learning the training instances are randomly selected to evaluate regardless of how much useful information they carry. This wastes effort on parts of the space that contribute little to the quality of the produced heuristic. Our approach, on the other hand, uses active learning to select and only focus on the most useful training examples. We demonstrate this technique by automatically constructing a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu–Gpu based heterogeneous system. Our methodology is remarkably simple and yet effective, making it a strong candidate for wide adoption. At high levels of classification accuracy the average learning speed-up is 3x, as compared to the state-of-the-art.

AB - Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data. However, obtaining this data can take months per platform. This is becoming an ever more critical problem and if no solution is found we shall be left with out of date heuristics which cannot extract the best performance from modern machines. In this work, we present a low-cost predictive modelling approach for automatic heuristic construction which significantly reduces this training overhead. Typically in supervised learning the training instances are randomly selected to evaluate regardless of how much useful information they carry. This wastes effort on parts of the space that contribute little to the quality of the produced heuristic. Our approach, on the other hand, uses active learning to select and only focus on the most useful training examples. We demonstrate this technique by automatically constructing a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu–Gpu based heterogeneous system. Our methodology is remarkably simple and yet effective, making it a strong candidate for wide adoption. At high levels of classification accuracy the average learning speed-up is 3x, as compared to the state-of-the-art.

KW - Machine learning

KW - Workload scheduling

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U2 - 10.1007/978-3-319-17473-0_10

DO - 10.1007/978-3-319-17473-0_10

M3 - Conference contribution

AN - SCOPUS:84937459076

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 160

BT - Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Revised Selected Papers

A2 - Brodman, James

A2 - Tu, Peng

PB - Springer Nature

T2 - 27th International Workshop on Languages and Compilers for Parallel Computing

Y2 - 15 September 2014 through 17 September 2014

ER -