BOAT: Building auto-tuners with structured Bayesian optimization
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Authors
Dalibard, V
Schaarschmidt, M
Yoneki, E
Publication Date
2017Journal Title
26th International World Wide Web Conference, WWW 2017
Conference Name
WWW '17: 26th International World Wide Web Conference
ISBN
9781450349130
Publisher
International World Wide Web Conferences Steering Committee
Pages
479-488
Type
Conference Object
Metadata
Show full item recordCitation
Dalibard, V., Schaarschmidt, M., & Yoneki, E. (2017). BOAT: Building auto-tuners with structured Bayesian optimization. 26th International World Wide Web Conference, WWW 2017, 479-488. https://doi.org/10.1145/3038912.3052662
Abstract
Due to their complexity, modern systems expose many con-figuration parameters which users must tune to maximizeperformance. Auto-tuning has emerged as an alternative inwhich a black-box optimizer iteratively evaluates configura-tions to find efficient ones. Unfortunately, for many systems,such as distributed systems, evaluating performance takestoo long and the space of configurations is too large for theoptimizer to converge within a reasonable time
Sponsorship
Engineering and Physical Sciences Research Council (EP/P004024/1)
Engineering and Physical Sciences Research Council (EP/M508007/1)
Engineering and Physical Sciences Research Council (EP/H003959/1)
Identifiers
External DOI: https://doi.org/10.1145/3038912.3052662
This record's URL: https://www.repository.cam.ac.uk/handle/1810/284899
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