Machine learning workflow optimization via automatic discovery of resource reuse opportunities
Liu, Jialin
Loading…
Permalink
https://hdl.handle.net/2142/104894
Description
Title
Machine learning workflow optimization via automatic discovery of resource reuse opportunities
Author(s)
Liu, Jialin
Issue Date
2019-04-22
Director of Research (if dissertation) or Advisor (if thesis)
Parameswaran, Aditya
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning
Deep Learning
System
Abstract
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
