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Paper Abstract and Keywords
Presentation 2016-08-09 11:15
A simulation study on fault tolerancy of parallel machine learning systems with parameter servers
Mingxi Li (Univ. of Tsukuba), Yusuke Tanimura, Hidemoto Nakada (AIST) CPSY2016-20 DC2016-17
Abstract (in Japanese) (See Japanese page) 
(in English) Parallel computation is essential for machine learning systems to be more faster.
There are two techniques to build parallel machine learning systems; namely data parallel method and model parallel method. In this paper, we only discuss data parallel where large number of parameter servers and computation servers communicate each other to perform computation. Fault tolerancy is a big problem on large scale computation system in general, however, there are not much discussions about the fault folerancy of parallel machine learning system. in this paper, we discuss the fault tolerancy of parallel machine learning systems which use parameter servers. Parameter servers gives extra redundancy to the system and could double as the checkpoint server. We also quantitatively evaluate several fault tolerance method using parallel environment simulator SimGrid.
Keyword (in Japanese) (See Japanese page) 
(in English) Fault Tolerancy / Parameter Server / Machine Learning / Simulations / Distributed Systems / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 177, CPSY2016-20, pp. 125-130, Aug. 2016.
Paper # CPSY2016-20 
Date of Issue 2016-08-01 (CPSY), 2016-08-02 (DC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CPSY2016-20 DC2016-17

Conference Information
Committee CPSY DC IPSJ-ARC  
Conference Date 2016-08-08 - 2016-08-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Kissei-Bunka-Hall (Matsumoto) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Parallel, Distributed and Cooperative Processing 
Paper Information
Registration To CPSY 
Conference Code 2016-08-CPSY-DC-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A simulation study on fault tolerancy of parallel machine learning systems with parameter servers 
Sub Title (in English)  
Keyword(1) Fault Tolerancy  
Keyword(2) Parameter Server  
Keyword(3) Machine Learning  
Keyword(4) Simulations  
Keyword(5) Distributed Systems  
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1st Author's Name Mingxi Li  
1st Author's Affiliation University of Tsukubay (Univ. of Tsukuba)
2nd Author's Name Yusuke Tanimura  
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
3rd Author's Name Hidemoto Nakada  
3rd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
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Speaker Author-1 
Date Time 2016-08-09 11:15:00 
Presentation Time 30 minutes 
Registration for CPSY 
Paper # CPSY2016-20, DC2016-17 
Volume (vol) vol.116 
Number (no) no.177(CPSY), no.178(DC) 
Page pp.125-130(CPSY), pp.1-6(DC) 
#Pages 6 
Date of Issue 2016-08-01 (CPSY), 2016-08-02 (DC) 

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