Machine learning workflow optimization via automatic discovery of resource reuse opportunities

Many state-of-the-art deep learning models rely on dynamic computation logic, making them difficult to optimize. In this thesis, we present a hashing based algorithm that is able to detect and optimize computation logic common to different computation graphs. We show that our algorithm can be integrated seamlessly into popular deep learning frameworks such as TensorFlow, with nearly zero code changes required on the part of users in order to adapt our optimizations to their programs. Experiments show that our algorithm achieves 1.35× speedup on a sentiment classification task trained with the popular Tree-LSTM model.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Liu, Jialin

Contributors dc:contributor

• Parameswaran, Aditya

• Machine Learning
• Deep Learning
• System

Statement dc:rights

• Copyright 2019 Jialin Liu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104894

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104894