Arnaud Legrand
5
Documents
Présentation
### Contact:
arnaud.legrand@imag.fr
### Webpage:
[Academic Page ](http://mescal.imag.fr/membres/arnaud.legrand/)
### Resume:
Arnaud Legrand is the leader of the [POLARIS](https://team.inria.fr/polaris/) team. He is a [CNRS](http://www.cnrs.fr/) research scientist at the [LIG](http://www.liglab.fr/). His research targets the management (mostly from an algorithmic point of view, i.e., scheduling, load balancing, fairness, game theory….) and performance evaluation (in particular through simulation, visualization, statistical analysis, …) of large scale distributed computing infrastructures such as clusters, grids, desktop grids, volunteer computing platforms, clouds,… when used for scientific computing. He is one of the main developers of the [**SimGrid**](http://simgrid.gforge.inria.fr/) project, a **simulation** toolkit for building simulators of distributed applications (originally designed for scheduling algorithm evaluation purposes) developed in collaboration with [Henri Casanova](http://navet.ics.hawaii.edu/%7Ecasanova/), [Martin Quinson](http://www.loria.fr/%7Equinson/) and [Frédéric Suter](http://graal.ens-lyon.fr/%7Efsuter/).
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### Education and Professional Experience
Nov. 2015*Habilitation à Diriger des Recherches*, University of GrenobleOct. 2004-…:Tenured Researcher for the CNRS (*Chargé de Recherche*) at Laboratoire d'Informatique de Grenoble.2004-2005:Post-Doctoral Research Associate, UCSD (USA). Collaboration with Henri Casanova, Larry Carter and Jeanne Ferrante.2003-2004:Post-Doctoral Research Associate, École Normale Supérieure de Lyon (France).2000-Dec. 2003:Ph.D. Computer Science, École Normale Supérieure de Lyon. Laboratoire de l'Informatique du Parallélisme. Thesis: *Heterogeneous parallel algorithms and scheduling : static and dynamic approaches* Advisors: Prof. Olivier Beaumont and Prof. Yves Robert.
Publications
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Simulation-based Optimization and Sensibility Analysis of MPI Applications: Variability MattersJournal of Parallel and Distributed Computing, 2022, ⟨10.1016/j.jpdc.2022.04.002⟩
Article dans une revue
hal-03141988v2
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Fast and Faithful Performance Prediction of MPI Applications: the HPL Case Study2019 IEEE International Conference on Cluster Computing (CLUSTER), Sep 2019, Albuquerque, United States. ⟨10.1109/CLUSTER.2019.8891011⟩
Communication dans un congrès
hal-02096571v4
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Predicting the Energy Consumption of MPI Applications at Scale Using a Single NodeCluster 2017, IEEE, Sep 2017, Hawaii, United States
Communication dans un congrès
hal-01523608v2
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Emulating High Performance Linpack on a Commodity Server at the Scale of a Supercomputer2017
Pré-publication, Document de travail
hal-01654804v1
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DGEMM performance is data-dependent[Research Report] RR-9310, Université Grenoble Alpes; Inria; CNRS. 2019
Rapport
hal-02401760v1
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