Welcome to my webpage !

I am a Temporary Assistant Professor at the Laboratoire d'Informatique Fondamentale d'Orléans
in Orléans University, France

Research

My Phd topic:

Functional abstraction for programming Multi-level architectures:

Formalisation and implementation
under the direction of Frédéric GAVA and Julien TESSON



2013-2017 Phd: Functional abstraction for programming Multi-level architectures (Université Paris Est)
2011-2013 MSc: Visualisation, Image processing and Performances (Université Orléans)
2010-2011 BSc: Computer Sciences (Université Orléans)

For more information, take a look at my CV(English)/CV(Français).

Publications

Journal:

Multi-ML: Programming Multi-BSP Algorithms in ML
V. Allombert, F. Gava, J. Tesson
International Journal of Parallel Programming. Springer Verlag (Germany) 2017 [HAL] BSP is a bridging model between abstract execution and concrete parallel systems. Structure and abstraction brought by BSP allow to have portable parallel programs with scalable performance predictions, without dealing with low-level details of architectures. In the past, we designed BSML for programming BSP algorithms in ML. However, the simplicity of the BSP model does not fit the complexity of today’s hierarchical architectures such as clusters of machines with multiple multi-core processors. The Multi-BSP model is an extension of the BSP model which brings a tree-based view of nested components of hierarchical architectures. To program Multi-BSP algorithms in ML, we propose the Multi-ML language as an extension of BSML where a specific kind of recursion is used to go through a hierarchy of computing nodes. We define a formal semantics of the language and present preliminary experiments which show performance improvements with respect to BSML.

International Conferences:

An Out-of-core GPU Approach for Accelerating Geostatistical Interpolation
V. Allombert, D. Michea, F. Dupros, C. Bellier, B. Bourgine, H. Aochi, S. Jubertie
International Conference on Computational Science. 2014. Cairns, Australia. [Link] Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70's, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Industries. Considering the huge amount of data available, standard implementations of these numerical methods are not efficient enough to tackle current challenges in geosciences. Moreover, most of the software packages available for geostatisticians are designed for a usage on a desktop computer due to the trial and error procedure used during the interpolation. The Geological Data Management (GDM) software package developed by the French geological survey (BRGM) is widely used to build reliable three-dimensional geological models that require a large amount of memory and computing resources. Considering the most time-consuming phase of kriging methodology, we introduce an efficient out-of-core algorithm that fully benefits from graphics cards acceleration on desktop computer. This way we are able to accelerate kriging on GPU with data 4 times bigger than a classical in-core GPU algorithm, with a limited loss of performances.

International Workshops

Multi-ML: Programming Multi-BSP Algorithms in ML
V. Allombert, F. Gava, J. Tesson
8th International Symposium on High-Level Parallel Programming and Applications (HLPP 2015) Link [PDF] BSP is a bridging model between abstract execution and concrete parallel systems. Structure and abstraction brought by BSP allow to have portable parallel programs with scalable performance predictions, without dealing with low-level details of architectures. In the past, we designed BSML for programming BSP algorithms in ml. However, the simplicity of the BSP model does not fit the complexity of today’s hierarchical architectures such as clusters of machines with multiple multi-core processors. The multi-BSP model is an extension of the BSP model which brings a tree-based view of nested components of hierarchical architectures. To program multi-BSP algorithms in ml, we propose the multi-ml language as an extension of BSML where a specific kind of recursion is used to go through a hierarchy of computing nodes. We define a formal semantics of the language and present preliminary experiments which show performance improvements with respect to BSML.

Thesis:

Functional abstraction for programming Multi-level architectures: Formalisation and implementation
7 July 2017. Paris. France [PDF] [slides] [jury] From personal computers using an increasing number of cores, to supercomputers having millions of computing units, parallel architectures are the current standard. The high performance architectures are usually referenced to as hierarchical, as they are composed from clusters of multi-processors of multi-cores. Programming such architectures is known to be notoriously difficult. Writing parallel programs is, most of the time, difficult for both the algorithmic and the implementation phase. To answer those concerns, many structured models and languages were proposed in order to increase both expressiveness and efficiency. Among other models, Multi-BSP is a bridging model dedicated to hierarchical architecture that ensures efficiency, execution safety, scalability and cost prediction. It is an extension of the well known BSP model that handles flat architectures. In this thesis we introduce the Multi-ML language, which allows programming Multi-BSP algorithms “à la ML” and thus, guarantees the properties of the Multi-BSP model and the execution safety, thanks to a ml type system. To deal with the multi-level execution model of Multi-ML, we defined formal semantics which describe the valid evaluation of an expression. To ensure the execution safety of Multi-ML programs, we also propose a typing system that preserves replicated coherence. An abstract machine is defined to formally describe the evaluation of a Multi-ML program on a Multi-BSP architecture. An implementation of the language is available as a compilation toolchain. It is thus possible to generate an efficient parallel code from a program written in Multi-ML and execute it on any hierarchical machine.

Acknowledgement:

Towards a self-consistent orbital evolution for EMRIs
A. Spallicci, P. Ritter, S. Jubertie, S. Cordier, S. Aoudia
IX Lisa Conference,Proceedings by the Astronomical Society of the Pacific Conference Serie. Paris. [Link] We intend to develop part of the theoretical tools needed for the detection of gravitational waves coming from the capture of a compact object, 1-100 solar masses, by a Supermassive Black Hole, up to a 10 billion solar masses, located at the centre of most galaxies. The analysis of the accretion activity unveils the star population around the galactic nuclei, and tests the physics of black holes and general relativity. The captured small mass is considered a probe of the gravitational field of the massive body, allowing a precise measurement of the particle motion up to the final absorption. The knowledge of the gravitational signal, strongly affected by the self-force - the orbital displacement due to the captured mass and the emitted radiation - is imperative for a successful detection. The results include a strategy for wave equations with a singular source term for all type of orbits. We are now tackling the evolution problem, first for radial fall in Regge- Wheeler gauge, and later for generic orbits in the harmonic or de Donder gauge for Schwarzschild-Droste black holes. In the Extreme Mass Ratio Inspiral, the determination of the orbital evolution demands that the motion of the small mass be continuously corrected by the self-force, i.e. the self-consistent evolution. At each of the integration steps, the self-force must be computed over an adequate number of modes; further, a differential-integral system of general relativistic equations is to be solved and the outputs regularised for suppressing divergences. Finally, for the provision of the computational power, parallelisation is under examination.

Teaching

2017-2018 High Performance Programming CM/TD/TP Doct. 6h
2017-2018 Multitier Programming TP Licence 3 20h
2017-2018 Data Bases TD Master 1 16h
2017-2018 Internship Licence 3 8h
2016-2018 Advanced Visualisation CM/TD/TP Master 2 39h
2016-2018 Operating Systems CTD Licence 1 60h
2016-2018 Algorithmic and Programming TD Licence 1 186h
2016-2017 Distributed Systems TD Master 1 33h
2016-2017 Application Design TP Licence 1 13h
2016-2017 Internship Master 2 4h
2015-2016 C Programming TD Licence 2 24h
2015-2016 Java Programming TD Licence 2 16.5h
2014-2015 Data Bases TD Licence 3 15h
2014-2015 Low-level Operating Systems TD Licence 2 24h
2013-2015 Computers Architectures TD Licence 2 72h
2013-2015 Experimental Algorithms TD Licence 1 50h
2013-2014 Functional Programming TD Licence 2 3h
Total: 589.5h

About Me

The Multi-ML project.


Past events:

2018: LIP - Avalon seminar, Lyon, France   
2017: GDR LaMHA, Orléans, France   
2016: GDR LTP, Paris, France   
2016: GDR GPL, Besançon, France   
2016: SIAM-PP16, Paris, France   
2015: BSPSP, Orléans, France   
2015: HLPP, Pisa, Italy   
2015: GDR GPL, Bordeaux, France   
2015: LACL seminar, Créteil, France   
2015: LaMHA, Créteil, France   
2014: ParaPhrase, Dublin, Ireland   
2014: EJCP, Rennes, France   

Program commitee:

  • APPMM 2018: Advances in Parallel Programming Models and Frameworks for the Multi-/Many-core Era (Part of HPCS 2018), Orléans, France 
  • ScalCom 2018: International Conference on Scalable Computing and Communications, Guangzhou, China 

Referee:

  • HLPP 2017: International Symposium on High-Level Parallel Programming and Applications, Valladolid, Spain 
  • ParCo 2017: Parallel Computing Conference, Bologna, Italy 

Organization:

  • HPCS 2018: International Conference on High Performance Computing & Simulation (Part of organization) Orléans, France 

Contact

E-mail:

Address: Bureau A11 (111-Rdc)
LIFO - Bâtiment IIIA
Rue Léonard de Vinci
F-45067 ORLEANS Cedex 2