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Equilibrage de charge et redistribution de données sur plates-formes hétérogènes

Abstract : In this thesis, we study iterative algorithms onto heterogeneous platforms. These iterative algorithms operate on large data samples (recursive convolution, image processing algorithms, etc.). At each iteration, independent calculations are carried out in parallel, and some communications take place. Note that there is no reason a priori to restrict to a uni-dimensional partitioning of the data, and to map it onto a uni-dimensional ring of processors. But uni-dimensional partitionings are very natural for most applications, and, as will be shown in this thesis, the problem to find the optimal one is already very difficult.
After dealing with the problems of mapping and load-balancing onto heterogeneous platforms, we consider the problem of redistributing data onto these platforms, an operation induced by possible variations in the resource performances (CPU speed, communication bandwidth) or in the system/application requirements (completed tasks, new tasks, migrated tasks, etc.).
For homogeneous rings the problem has been completely solved. Indeed, we have designed optimal algorithms, and provided formal proofs of correctness, both for unidirectional and bidirectional rings. For heterogeneous rings there remains further research to be conducted. The unidirectional case was easily solved, but the bidirectional case remains open. Still, we have derived an optimal solution for light redistributions, an important case in practice.
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Contributor : Hélène Renard <>
Submitted on : Thursday, April 13, 2006 - 11:24:02 AM
Last modification on : Wednesday, November 20, 2019 - 2:51:31 AM
Long-term archiving on: : Saturday, April 3, 2010 - 10:17:21 PM


  • HAL Id : tel-00012133, version 1



Hélène Renard. Equilibrage de charge et redistribution de données sur plates-formes hétérogènes. Autre [cs.OH]. Ecole normale supérieure de lyon - ENS LYON, 2005. Français. ⟨tel-00012133⟩



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