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Ph.D de

Ph.D
Group : Parallel Systems

Méthodes de préconditionnement pour la résolution de systèmes linéaires sur des machines massivement parallèles

Starts on 10/09/2010
Advisor : GRIGORI, Laura
[GRIGORI Laura]

Funding :
Affiliation : Université Paris-Sud
Laboratory : LRI Grand Large

Defended on 10/04/2014, committee :
Directeur de la thèse
Laura Grigori, Directeur de Recherche, INRIA Rocquencourt

Rapporteurs
Olaf Schenk, Professeur, Institute of Computational Science, Universita della Svizzera italiana
Serge Gratton, Professeur, INPT/ENSEEIHT

Examinateurs
Pascal Henon, Ingenieur de recherche, Total
Yannis Manoussakis, Professeur, Paris 11, LRI
Frederic Nataf, Directeur de Recherche, CNRS Paris 6, Laboratoire J.L. Lions

Research activities :

Abstract :
This thesis addresses a new class of preconditioners which aims at accelerating solving large sparse systems arising in scientific and engineering problem by using preconditioned iterative methods. To apply these preconditioners, the input matrix needs to be reordered with K-way nested dissection. We also introduce an overlapping technique that adapts the idea of overlapping subdomains from domain decomposition methods to nested dissection based methods to improve the convergence of these preconditioners. Results show that such overlapping technique improves the convergence rate of Nested SSOR (NSSOR) and Nested Modified Incomplete LU with Rowsum property (NMILUR) precondtioners that we worked on. We also present the data distribution and parallel algorithms for implementing these preconditioners. Results show that on a 400x400x400 regular grid, the number of iterations with Nested Filtering Factorization preconditioner (NFF) increases slightly while increasing the number of subdomains up to 2048. In terms of runtime performance on Curie supercomputer, it scales up to 2048 cores and it is 2.6 times faster than the domain decomposition preconditioner Restricted Additive Schwarz (RAS) as implemented in PETSc.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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