Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Partnerships and Cooperations

National Initiatives


The ELCI PIA project (Software Environment for HPC) aims to develop a new generation of software stack for supercomputers, numerical solvers, runtime and programming development environments for HPC simulation. The ELCI project also aims to validate this software stack by showing its capacity to offer improved scalability, resilience, security, modularity and abstraction on real applications. The coordinator is Bull, and the different partners are CEA, Inria, SAFRAN, CERFACS, CNRS CORIA, CENAERO, ONERA, UVSQ, Kitware and AlgoTech.




  • ANR MONU 2013 Program, 2013 - 2018 (36 months extended )

  • Identification: ANR-13-MONU-0007

  • Coordinator: Inria Bordeaux/LaBRI

  • Other partners: CNRS-IRIT, Inria-LIP Lyon, CEA/CESTA, EADS-IW

  • Abstract: This project aims at studying and designing algorithms and parallel programming models for implementing direct methods for the solution of sparse linear systems on emerging computers equipped with accelerators. The ultimate aim of this project is to achieve the implementation of a software package providing a solver based on direct methods for sparse linear systems of equations. Several attempts have been made to accomplish the porting of these methods on such architectures; the proposed approaches are mostly based on a simple offloading of some computational tasks (the coarsest grained ones) to the accelerators and rely on fine hand-tuning of the code and accurate performance modeling to achieve efficiency. This project proposes an innovative approach which relies on the efficiency and portability of runtime systems, such as the StarPU tool developed in the runtime team (Bordeaux). Although the SOLHAR project will focus on heterogeneous computers equipped with GPUs due to their wide availability and affordable cost, the research accomplished on algorithms, methods and programming models will be readily applicable to other accelerator devices such as ClearSpeed boards or Cell processors.

  • AAPG ANR 2018 (42 months)

  • Coordinator: Yves Coudière (Carmen) Inria Bordeaux

  • Abstract: Cardiac arrhythmia affect millions of patients and cause 300,000 deaths each year in Europe. Most of these arrhythmia are due to interaction between structural and electrophysiological changes in the heart muscle. A true understanding of these phenomena requires numerical simulations at a much finer resolution, and larger scale, than currently possible. Next-generation, heterogeneous, high-performance computing (HPC) systems provide the power for this. But the large scale of the computations pushes the limits of current runtime optimization systems, and together with task-based parallelism, prompts for the development of dedicated numerical methods and HPC runtime optimizations. With a consortium including specialists of these domains and cardiac modeling, we will investigate new task-based optimization techniques and numerical methods to utilize these systems for cardiac simulations at an unprecedented scale, and pave the way for future use cases.

ADT - Inria Technological Development Actions



Participants : Raymond Namyst, Pierre-André Wacrenier, Andra Hugo, Brice Goglin, Corentin Salingue.

  • Inria ADT Campaign 2017, 10/2017 - 9/2019 (24 months)

  • Coordinator: Raymond Namyst

  • Abstract: The Inria action ADT SwLoc is aiming at developing a library allowing dynamic flexible partitioning of computing resources in order to execute parallel regions concurrently inside the same processes.

ADT Gordon


Participants : Denis Barthou, Nathalie Furmento, Samuel Thibault, Pierre-André Wacrenier.

  • Inria ADT Campaign 2018, 11/2018 - 11/2020 (24 months)

  • Coordinator: Emmanuel Jeannot (Tadaam)

  • Other partners: HiePACS, PLEIADE, Tadaam (Inria Bordeaux)

  • Abstract: Teams HiePACS, Storm and Tadaam develop each a brick of an HPC software stack, namely solver, runtime, and communication library. The goal of the Gordon project is to consolidate the HPC stack, to improve interfaces between each brick, and to target a better scalability. The bioinformatics application involved in the project has been selected so as to stress the underlying systems.



Participants : Denis Barthou, Olivier Aumage, Adrien Cassagne, Kun He.

  • Inria ADT Campaign 2018, 12/2018 - 12/2019 (12 months)

  • Coordinator: Denis Barthou

  • Other partners: C.Jégo and C.Leroux (IMS lab, U.Bordeaux)

  • Abstract: AFF3CT is a toolchain for designing, validation and experimentation of new Error Correcting codes. This toolchain is written in C++, and this constitutes a difficulty for many industrial users, who are mostly electronics engineers. The goal of this ADT is to widen the number of possible users by designing a Matlab and Python interface for AFF3CT, in collaboration with existing users, and proposing a parallel framework in OpenMP.

IPL - Inria Project Lab


(High-performance Application and Computers, Studying PErformance and Correctness In Simulation)

Participants : Samuel Thibault, Emmanuelle Saillard, Olivier Aumage, Idriss Daoudi.

  • Inria IPL 2016 - 2020 (48 months)

  • Coordinator: Arnaud Legrand (team Polaris, Inria Rhône Alpes)

Since June 2016, the team is participating to the HAC-SPECIS Inria Project Lab (IPL). This national initiative aims at answering methodological needs of HPC application and runtime developers and allowing to study real HPC systems both from the correctness and performance point of view. To this end, it gathers experts from the HPC, formal verification and performance evaluation community.


(High Performance Computing and Big Data)

Participant : Samuel Thibault.

  • Inria IPL 2018 - 2022 (48 months)

  • Coordinator: Bruno Raffin (team DataMove, Inria Rhône Alpes)

Since June 2018, the team is participating to the HPC-BigData Inria Project Lab (IPL). The goal of this HPC-BigData IPL is to gather teams from the HPC, Big Data and Machine Learning (ML) areas to work at the intersection between these domains. Research is organized along three main axes: high performance analytics for scientific computing applications, high performance analytics for big data applications, infrastructure and resource management.