A Survey of Techniques for Optimizing Deep Learning on GPUs

Mittal, Sparsh and Vaishay, Shraiysh (2019) A Survey of Techniques for Optimizing Deep Learning on GPUs. Journal of Systems Architecture. p. 101635. ISSN 13837621

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Abstract

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to its unique features, the GPU continues to remain the most widely used accelerator for DL applications. In this paper, we present a survey of architecture and system-level techniques for optimizing DL applications on GPUs. We review techniques for both inference and training and for both single GPU and distributed system with multiple GPUs. We bring out the similarities and differences of different works and highlight their key attributes. This survey will be useful for both novice and experts in the field of machine learning, processor architecture and high-performance computing.

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IITH Creators:
IITH CreatorsORCiD
Mittal, Sparshhttp://orcid.org/0000-0002-2908-993X
Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 28 Aug 2019 04:24
Last Modified: 28 Aug 2019 04:24
URI: http://raiith.iith.ac.in/id/eprint/6057
Publisher URL: http://doi.org/10.1016/j.sysarc.2019.101635
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