Result: An experimental validation of the PRO model for parallel and distributed computation

Title:
An experimental validation of the PRO model for parallel and distributed computation
Authors:
Essaïdi, Mohamed, Gustedt, Jens
Contributors:
Simulation, modeling and analysis of heterogeneous systems (SMASH), Centre Inria d'Université Côte d'Azur, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Provence - Aix-Marseille 1, 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), Beniamino Di Martino, Grid'5000
Source:
14th Euromicro International Conference on Parallel. :449-456
Publisher Information:
CCSD; IEEE, 2006.
Publication Year:
2006
Collection:
collection:CNRS
collection:INRIA
collection:UNIV-AMU
collection:INPL
collection:INRIA-SOPHIA
collection:INRIASO
collection:INRIA_TEST
collection:INRIA-LORRAINE
collection:LORIA2
collection:INRIA-NANCY-GRAND-EST
collection:GRID5000
collection:TESTALAIN1
collection:UNIV-LORRAINE
collection:INRIA2
collection:LORIA
collection:UNIV-COTEDAZUR
collection:SLICES-FR
collection:AM2I-UL
Subject Terms:
ACM: D.: Software, D.1: PROGRAMMING TECHNIQUES, D.1.3: Concurrent Programming, D.1.3.0: Distributed programming, D.2: SOFTWARE ENGINEERING, D.2.8: Metrics, D.2.8.1: Performance measures, ACM: F.: Theory of Computation, F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, F.2.2: Nonnumerical Algorithms and Problems, F.2.2.6: Sorting and searching, [INFO.INFO-DC]Computer Science [cs], Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-DS]Computer Science [cs], Data Structures and Algorithms [cs.DS], [INFO.INFO-PF]Computer Science [cs], Performance [cs.PF]
Subject Geographic:
Montbeliard-Sochaux, France
Original Identifier:
HAL:
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
Access URL:
https://inria.hal.science/inria-00000612
https://inria.hal.science/inria-00000612v1/document
https://inria.hal.science/inria-00000612v1/file/PRO-experiments.pdf
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00000612v1
Database:
HAL

Further Information

National audience
The Parallel Resource-Optimal (PRO) computation model was introduced by Gebremedhin et al. [2002] as a framework for the design and analysis of efficient parallel algorithms. The key features of the PRO model that distinguish it from previous parallel computation models are the full integration of resource-optimality into the design process and the use of a {granularity function as a parameter for measuring quality. In this paper we present experimental results on parallel algorithms, designed using the PRO model, for two representative problems: list ranking and sorting. The algorithms are implemented using SSCRAP, our environment for developing coarse-grained algorithms. The experimental performance results observed agree well with analytical predictions using the PRO model. Moreover, by using different platforms to run our experiments, we have been able to provide an integrated view of the modeling of an underlying architecture and the design and implementation of scalable parallel algorithms.