A low communication overhead parallel implementation of the back-propagation algorithm
The back-propagation algorithm is one of the most widely used training algorithms for neural networks. The training phase of a multilayer perceptron by using this algorithm can take very long time making neural networks difficult to accept. One approach to solve this problem consists in the parallel...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2000
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23442 |
| Aporte de: |
| id |
I19-R120-10915-23442 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Neural nets Parallel |
| spellingShingle |
Ciencias Informáticas Neural nets Parallel Alfonso, Marcelo Kavka, Carlos Printista, Alicia Marcela A low communication overhead parallel implementation of the back-propagation algorithm |
| topic_facet |
Ciencias Informáticas Neural nets Parallel |
| description |
The back-propagation algorithm is one of the most widely used training algorithms for neural networks. The training phase of a multilayer perceptron by using this algorithm can take very long time making neural networks difficult to accept. One approach to solve this problem consists in the parallelization of the training algorithm.
There exists many different approaches, however most of them are well adapted to specialized hardware.
The idea to use a network of workstations as a general purpose parallel computer is widely accepted.
However, the communication overhead imposes restrictions in the design of parallel algorithms.
In this work, we propose a parallel implementation of the back-propagation algorithm that is suitable to be applied to a network of workstations. The objective is twofold. The first goal is to increment the performance of the training phase of the algorithm with low communication overhead. The second goal is to provide a dynamic assignment of tasks to processors in order to make the best use of the computational resources. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Alfonso, Marcelo Kavka, Carlos Printista, Alicia Marcela |
| author_facet |
Alfonso, Marcelo Kavka, Carlos Printista, Alicia Marcela |
| author_sort |
Alfonso, Marcelo |
| title |
A low communication overhead parallel implementation of the back-propagation algorithm |
| title_short |
A low communication overhead parallel implementation of the back-propagation algorithm |
| title_full |
A low communication overhead parallel implementation of the back-propagation algorithm |
| title_fullStr |
A low communication overhead parallel implementation of the back-propagation algorithm |
| title_full_unstemmed |
A low communication overhead parallel implementation of the back-propagation algorithm |
| title_sort |
low communication overhead parallel implementation of the back-propagation algorithm |
| publishDate |
2000 |
| url |
http://sedici.unlp.edu.ar/handle/10915/23442 |
| work_keys_str_mv |
AT alfonsomarcelo alowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm AT kavkacarlos alowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm AT printistaaliciamarcela alowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm AT alfonsomarcelo lowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm AT kavkacarlos lowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm AT printistaaliciamarcela lowcommunicationoverheadparallelimplementationofthebackpropagationalgorithm |
| bdutipo_str |
Repositorios |
| _version_ |
1764820465917886464 |