A cost-based optimizer for gradient descent optimization

Zoi Kaoudi, Jorge Arnulfo Quiané-Ruiz, Saravanan Thirumuruganathan, Sanjay Chawla, Divy Agrawal

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

24 Scopus citations

Abstract

As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Furthermore, these optimization problems can be efficiently solved using variations of the gradient descent (GD) algorithm. Thus, to decouple a user specification of an ML task from its execution, a key component is a GD optimizer. We propose a cost-based GD optimizer that selects the best GD plan for a given ML task. To build our optimizer, we introduce a set of abstract operators for expressing GD algorithms and propose a novel approach to estimate the number of iterations a GD algorithm requires to converge. Extensive experiments on real and synthetic datasets show that our optimizer not only chooses the best GD plan but also allows for optimizations that achieve orders of magnitude performance speed-up.

Original languageEnglish
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages977-992
Number of pages16
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Externally publishedYes
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Conference

Conference2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
Country/TerritoryUnited States
CityChicago
Period05/14/1705/19/17

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