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2021 | OriginalPaper | Chapter

Accelerating Machine Learning Algorithms with TensorFlow Using Thread Mapping Policies

Authors : Matheus W. Camargo, Matheus S. Serpa, Danilo Carastan-Santos, Alexandre Carissimi, Philippe O. A. Navaux

Published in: High Performance Computing

Publisher: Springer International Publishing

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Abstract

Machine Learning (ML) algorithms are increasingly being used in various scientific and industrial problems, with the time of execution of these algorithms as an important concern. In this work, we explore mappings of threads in multi-core architectures and their impact on new ML algorithms running with Python and TensorFlow. Using smart thread mapping, we were able to reduce the execution time of both training and inference phases for up to 46% and 29%, respectively.

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Footnotes
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Metadata
Title
Accelerating Machine Learning Algorithms with TensorFlow Using Thread Mapping Policies
Authors
Matheus W. Camargo
Matheus S. Serpa
Danilo Carastan-Santos
Alexandre Carissimi
Philippe O. A. Navaux
Copyright Year
2021
Book
High Performance Computing

Print ISBN: 978-3-030-68034-3

Electronic ISBN: 978-3-030-68035-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68035-0_5

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