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Coarse grain parallelization of deep neural networks

Autor
Gonzalez, M.
Tipus d'activitat
Article en revista
Revista
ACM SIGPLAN notices
Data de publicació
2016-08-01
Volum
51
Número
8
Pàgina inicial
Article No. 1
DOI
https://doi.org/10.1145/2851141.2851158 Obrir en finestra nova
URL
http://dl.acm.org/citation.cfm?doid=2851141.2851158 Obrir en finestra nova
Resum
Deep neural networks (DNN) have recently achieved extraordinary results in domains like computer vision and speech recognition. An essential element for this success has been the introduction of high performance computing (HPC) techniques in the critical step of training the neural network. This paper describes the implementation and analysis of a network-agnostic and convergence-invariant coarse-grain parallelization of the DNN training algorithm. The coarse-grain parallelization is achieved th...
Paraules clau
Coarse-grain parallelism, Deep learning, OpenMP, Performance, Shared memory algorithms, Stochastic gradient descent
Grup de recerca
CAP - Grup de Computació d'Altes Prestacions

Participants