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AIT ABA Massinissa

PhD graduated (ATER, Sorbonne Université - Faculté des Sciences)
Team : ALSOC
Localisation : Campus Pierre et Marie Curie
    Sorbonne Université - LIP6
    Boîte courrier 169
    Couloir 24-25, Étage 4, Bureau 404
    4 place Jussieu
    75252 PARIS CEDEX 05
    FRANCE
Tel: +33 1 44 27 71 24, Massinissa.Ait-Aba (at) nulllip6.fr
Supervision : Alix MUNIER
Co-supervision : ZAOURAR Lilia

Optimisation de l’énergie et de la performance d’applications sur des micro-servers hétérogènes

Recent applications, both in industry and research often need massive calculations. They have different hardware requirements in terms of computing speed, which leads to very high energy consumption of hardware platforms. Heterogeneous computing platforms offer a good compromise with high computing power while preserving the energy consumed to run high-performance parallel applications. They are therefore nowadays an interesting computing resource. In order to exploit the advantages offered by heterogeneity in terms of performance, efficient and automatic management of computing resources is becoming increasingly important to execute parallel applications. These new architectures have thus given rise to new scheduling problems that allocate and sequence calculations on the different resources by optimizing one or more criteria.
The objective of this thesis is to determine an efficient scheduling of a parallel application on a heterogeneous resource system in order to minimize the total execution time (makespan) of the application while respecting an energy constraint.
Two classes of heterogeneous platforms have been considered in our work: fully heterogeneous architectures that combine several processing elements (CPUs, GPUs, FPGAs), and hybrid platforms limited to two types of processors (CPU + GPU for example). We propose several application scheduling strategies on both platforms with two execution models. Preliminary experiments with the proposed algorithms using different applications and platforms of different sizes have shown good results compared to existing methods in the literature.
Defence : 06/04/2020 - 13h30 - Visioconférence
Jury members :
M Loris MARCHAL, CNRS, Univ. Lyon, LIP, France. [Rapporteur]
M Jean-Marc NICOD, CNRS, Univ. Bourgogne Franche-Comte, UTBM, France [Rapporteur]
M Lionel LACASSAGNE, LIP6, Sorbonne Université, France.
Mme Safia KEDAD-SIDHOUM, CNAM, CEDRIC, Paris, France.
M Guillaume PALLEZ, Inria, Labri & Univ. of Bordeaux, France.
M Denis TRYSTRAM, CNRS, Inria, Grenoble INP, LIG, France.
Mme Lilia ZAOURAR, LCE, CEA LIST, France.
Mme Alix MUNIER KORDON, LIP6, Sorbonne Université, France.

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