FlexGP 2.0 : multiple levels of parallelism in distributed machine learning via genetic programming
Name
871002391-MIT.pdf
Description
Full printable version
Size
22.89 MB
Format
Adobe PDF
Checksum (MD5)
3e9d66828a0f02ce217688a68c6becce
Author(s)
Sherry, Dylan J. (Dylan Jacob)
Advisor(s)
Kalyan Veeramachaneni and Una-May O'Reilly.
Alternative Title
Multiple levels of parallelism in distributed machine learning via genetic programming
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
This thesis presents FlexGP 2.0, a distributed cloud-backed machine learning system. FlexGP 2.0 features multiple levels of parallelism which provide a significant improvement in accuracy v.s. elapsed time. The amount of computational resources in FlexGP 2.0 can be scaled along several dimensions to support large, complex data. FlexGP 2.0's core genetic programming (GP) learner includes multithreaded C++ model evaluation and a multi-objective optimization algorithm which is extensible to pursue any number of objectives simultaneously in parallel. FlexGP 2.0 parallelizes the entire learner to obtain a large distributed population size and leverages communication between learners to increase performance via transferral of search progress between learners. FlexGP 2.0 factors training data to boost performance and enable support for increased data size and complexity. Several experiments are performed which verify the efficacy of FlexGP 2.0's multilevel parallelism. Experiments run on a large dataset from a real-world regression problem. The results demonstrate both less time to achieve the same accuracy and overall increased accuracy, and illustrate the value of FlexGP 2.0 as a platform for machine learning.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 105-107).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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