SparCML: High-Performance Sparse Communication for Machine Learning

Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data parallel" distributed across many nodes. Each node's contribution to the overall gradient is summed using a gl...

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Bibliographic Details

Published in:	SC19: International Conference for High Performance Computing, Networking, Storage and Analysis pp. 1 - 15
Main Authors:	Renggli, Cedric, Ashkboos, Saleh, Aghagolzadeh, Mehdi, Alistarh, Dan, Hoefler, Torsten
Format:	Conference Proceeding
Language:	English
Published:	ACM 17.11.2019
Subjects:	Distributed databases High performance computing Industries Libraries Machine learning Machine learning algorithms Protocols Scalability Sparse AllGather Sparse AllReduce Sparse Input Vectors Vectors
ISSN:	2167-4337
Online Access:	Get full text
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