Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Mohammadi:2019:10.1109/ICASSP.2019.8682911,
author = {Mohammadi, Amiri M and Gunduz, D},
doi = {10.1109/ICASSP.2019.8682911},
pages = {8177--8181},
publisher = {Institute of Electrical and Electronics Engineers},
title = {Computation scheduling for distributed machine learning with straggling workers},
url = {http://dx.doi.org/10.1109/ICASSP.2019.8682911},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We study scheduling of computation tasks acrossnworkers in a large scale distributed learning problem. Computa-tion speeds of the workers are assumed to be heterogeneous andunknown to the master, and redundant computations are assignedto  the  workers  in  order  to  tolerate  straggling  workers.  We  con-sider  sequential  computation  and  instantaneous  communicationfrom  each  worker  to  the  master,  and  each  computation  round,which  can  model  a  single  iteration  of  the  stochastic  gradientdescent  (SGD)  algorithm,  iscompletedonce  the  master  receivesk≤ndistinct  computations,  referred  to  as  thecomputationtarget.  Our  goal  is  to  characterize  theaverage  completion  timeas a function of thecomputation load, which denotes the portionof  the  dataset  available  at  each  worker,  and  the  computationtarget.  We  propose  two  computation  scheduling  schemes  thatspecify  the  computation  tasks  assigned  to  each  worker,  as  wellas their order of execution. We also establish a lower bound onthe  minimum  average  completion  time.  Numerical  results  showa significant reduction in the average computation time over theexisting  coded  and  uncoded  computing  schemes.
AU  - Mohammadi,Amiri M
AU  - Gunduz,D
DO  - 10.1109/ICASSP.2019.8682911
EP  - 8181
PB  - Institute of Electrical and Electronics Engineers
PY  - 2019///
SN  - 2379-190X
SP  - 8177
TI  - Computation scheduling for distributed machine learning with straggling workers
UR  - http://dx.doi.org/10.1109/ICASSP.2019.8682911
UR  - http://hdl.handle.net/10044/1/68313
ER  -
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