PAGALINNET

Parallel Grid-aware Library for Neural Networks Training

 Coordinatore UNIVERSITA DELLA CALABRIA 

 Organization address address: Via Pietro Bucci 7/11 B
city: ARCAVACATA DI RENDE
postcode: 87036

contact info
Titolo: Prof.
Nome: Lucio
Cognome: Grandinetti
Email: send email
Telefono: -495676
Fax: -495792

 Nazionalità Coordinatore Italy [IT]
 Totale costo 231˙604 €
 EC contributo 231˙604 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2007-4-2-IIF
 Funding Scheme MC-IIF
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-04-01   -   2011-03-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITA DELLA CALABRIA

 Organization address address: Via Pietro Bucci 7/11 B
city: ARCAVACATA DI RENDE
postcode: 87036

contact info
Titolo: Prof.
Nome: Lucio
Cognome: Grandinetti
Email: send email
Telefono: -495676
Fax: -495792

IT (ARCAVACATA DI RENDE) coordinator 0.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

pattern    networks    parallel    computational    batch    heterogeneous    host    barrier    library    efficiency    grids    single    grid    algorithms    architecture    neural    parallelization    software    network    matching    training    enhanced    experimentally    return   

 Obiettivo del progetto (Objective)

'The proposed research is focused on the software library development for parallel neural networks training on computational Grids. The main scientific reason of the proposed research is to develop enhanced parallel neural network training algorithms which provide better parallelization efficiency on heterogeneous computational Grids in the contrast to existing algorithms. The objectives of the proposed research are: 1. to adapt the computational cost model of parallel neural network training algorithms within single pattern, batch pattern and modular approaches to heterogeneous computational Grid resources of host institution; 2. to develop enhanced single pattern and batch pattern parallel neural network training algorithms based on improved communication and barrier functions; 3. to develop a method of automatic matching of parallelization strategy to architecture of appropriate parallel computing system; 4. to develop parallel Grid-aware library for neural networks training capable to use heterogeneous computational resources; 5. to test experimentally parallel Grid-aware library for neural networks training on heterogeneous computational Grid system of host institution within the tasks of one of its active projects; 6. to deploy parallel Grid-aware library for neural networks training on the computational Grid of return host; 7. to test experimentally parallel Grid-aware library on computational systems of both host institution and return host. The cost models of the algorithms will be developed using computational complexity approaches, improved barrier and reducing function will be adapted to neural network parallelization schemes, optimization strategies will be used to find best matching “architecture of parallel system – neural network parallelization scheme”, software library will be implemented on C programming language and MPI parallelization, the efficiency of parallel algorithm will be assessed in comparison with sequential implementation.'

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