DOGEAS Konstantinos

PhD graduated
Team : RO
Departure date : 04/06/2022

Supervision : Evripidis BAMPIS

Co-supervision : PASCUAL Fanny

Minimisation de l'énergie, mouvements de données, et données incertaines: modèles et algorithmes

High performance computers (HPCs) is the go-to solution for running computationally demanding applications.
As the limit of energy consumption is already achieved, the need for more energy efficient algorithms is critical.
Taking advantage of the core characteristics of an HPC, such as its network topology and the heterogeneity of the machines, could lead to better scheduling algorithms. In addition, designing more realistic models, that grasp the features of real-life applications, is a work in the same direction of achieving better performance.
Allowing scheduling algorithms to decide either the amount of resources allocated to an application or the running speed of the resources can pave the path to new platform-aware implementations.
We also deal with the uncertainty on part of the input and more specifically, the workload of an application, that is strictly related to the time needed for its completion. Most works in the literature consider this value known in advance. However, this is rarely the case in real-life systems.

Defence : 04/06/2022

Jury members :

Thomas Erlebach, Durham University [Rapporteur]
Dimitris Fotakis, National and Technical University of Athens [Rapporteur]
Pierre Sens, Sorbonne University
Denis Trystram, University Grenoble-Alpes
Evripidis Bampis, Sorbonne University
Giorgio Lucarelli, University of Lorraine
Fanny Pascual, Sorbonne University

Departure date : 04/06/2022

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