Scalability Analysis and Optimization for Large-Scale Deep Learning

Scalability Analysis and Optimization for Large-Scale Deep Learning

Despite its growing importance, scalable deep learning (DL) remains a difficult challenge. Scalability of large-scale DL is constrained by many factors, including those deriving from data movement and data processing. DL frameworks rely on large volumes of data to be fed to the computation engines...

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Bibliographic Details
Main Author: Pumma, Sarunya
Other Authors: Computer Science
Format: Others
Published: Virginia Tech 2021
Subjects:
Scalable Deep Learning
Data Movement Investigation
Parallel I/O Optimization
Straggler Processes
Computational Imbalance
Resource Contention
Online Access:http://hdl.handle.net/10919/104417

Internet

http://hdl.handle.net/10919/104417

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