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Memory-aware Algorithms and Scheduling Techniques for Matrix Computations

Julien Herrmann 1, 2
Abstract : Throughout this thesis, we have designed memory-aware algorithms and scheduling techniques suitedfor modern memory architectures. We have shown special interest in improving the performance ofmatrix computations on multiple levels. At a high level, we have introduced new numerical algorithmsfor solving linear systems on large distributed platforms. Most of the time, these linear solvers rely onruntime systems to handle resources allocation and data management. We also focused on improving thedynamic schedulers embedded in these runtime systems by adding static information to their decisionprocess. We proposed new memory-aware dynamic heuristics to schedule workflows, that could beimplemented in such runtime systems.Altogether, we have dealt with multiple state-of-the-art factorization algorithms used to solve linearsystems, like the LU, QR and Cholesky factorizations. We targeted different platforms ranging frommulticore processors to distributed memory clusters, and worked with several reference runtime systemstailored for these architectures, such as P A RSEC and StarPU. On a theoretical side, we took specialcare of modelling convoluted hierarchical memory architectures. We have classified the problems thatare arising when dealing with these storage platforms. We have designed many efficient polynomial-timeheuristics on general problems that had been shown NP-complete beforehand.
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https://tel.archives-ouvertes.fr/tel-01241485
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Submitted on : Thursday, December 10, 2015 - 3:33:05 PM
Last modification on : Wednesday, November 20, 2019 - 3:03:19 AM
Long-term archiving on: : Friday, March 11, 2016 - 6:09:33 PM

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Julien Herrmann. Memory-aware Algorithms and Scheduling Techniques for Matrix Computations. Distributed, Parallel, and Cluster Computing [cs.DC]. Ecole normale supérieure de lyon - ENS LYON, 2015. English. ⟨NNT : 2015ENSL1043⟩. ⟨tel-01241485⟩

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