Implementation of the AdaBoost Algorithm for Large Scale Distributed Environments: Comparing JavaSpace and MPJ
| Autor: | Stéphane Genaud, Virginie Galtier, Stéphane Vialle |
|---|---|
| Přispěvatelé: | SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Algorithms for the Grid (ALGORILLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Grid'5000 |
| Rok vydání: | 2009 |
| Předmět: |
Distributed Computing Environment
JavaSpace Grid Computing Computer science Adaboost Distributed computing Message passing Parallel algorithm Parameterized complexity 02 engineering and technology Parallel computing computer.software_genre Grid computing 020204 information systems 0202 electrical engineering electronic engineering information engineering Resource allocation Resource allocation (computer) 020201 artificial intelligence & image processing MPJ AdaBoost [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] computer Java |
| Zdroj: | ICPADS Fifteenth International Conference on Parallel and Distributed Systems (ICPADS'09) Fifteenth International Conference on Parallel and Distributed Systems (ICPADS'09), Dec 2009, Shenzhen, China. ⟨10.1109/ICPADS.2009.67⟩ |
| DOI: | 10.1109/icpads.2009.67 |
| Popis: | International audience; This paper presents the parallelization of a machine learning method, called the adaboost algorithm. The parallel algorithm follows a dynamically load-balanced master-worker strategy, which is parameterized by the granularity of the tasks distributed to workers. We first show the benefits of this version with heterogeneous processors. Then, we study the application in a real, geographically distributed environment, hence adding network latencies to the execution. Performances of the application using more than a hundred processes are analyzed in both JavaSpace and {\pmpi}. We therefore present an head-to-head comparison of two parallel programming models. We study for each case the granularities yielding the best performance. We show that current network technologies enable to obtain interesting speedups in many situations for such an application, even when using a virtual shared memory paradigm in a large-scale distributed environment. |
| Databáze: | OpenAIRE |
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