Section: Partnerships and Cooperations

National Initiatives

ANR COOP

Multi-level Cooperative Resource Management (http://coop.gforge.inria.fr/ ).

• ANR COSINUS 2009 Program, 12/2009 - 06/2013 (42 months)

• Identification: ANR-09-COSI-001

• Coordinator: Christian Pérez (Inria Rhône-Alpes)

• Other partners: Inria Bordeaux, Inria Rennes, IRIT, EDF R&D.

• Abstract: COOP aims at establishing generic cooperation mechanisms between resource management, runtime systems, and application programming frameworks to simplify programming models, and improve performance through adaptation to the resources.

ANR SOLHAR

(http://solhar.gforge.inria.fr/doku.php?id=start ).

• ANR MONU 2013 Program, 2013 - 2016 (36 months)

• 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.

ANR Songs

Simulation of next generation systems (http://infra-songs.gforge.inria.fr/ ).

• ANR INFRA 2011, 01/2012 - 12/2015 (48 months)

• Identification: ANR-11INFR01306

• Coordinator: Martin Quinson (Inria Nancy)

• Other partners: Inria Nancy, Inria Rhône-Alpes, IN2P3, LSIIT, Inria Rennes, I3S.

• Abstract: The goal of the SONGS project is to extend the applicability of the SimGrid simulation framework from Grids and Peer-to-Peer systems to Clouds and High Performance Computation systems. Each type of large-scale computing system will be addressed through a set of use cases and lead by researchers recognized as experts in this area.

ANR MOEBUS

Sceduling in HPC (http://moebus.gforge.inria.fr/doku.php ).

• ANR INFRA 2013, 10/2013 - 9/2017 (48 months)

• Coordinator: Denis Trystram (Inria Rhône-Alpes)

• Other partners: Inria Bordeaux.

• Abstract: This project focuses on the efficient execution of parallel applications submitted by various users and sharing resources in large-scale high-performance computing environments

Since January 2013, the team is participating to the C2S@Exa http://www-sop.inria.fr/c2s_at_exa Inria Project Lab (IPL). This national initiative aims at the development of numerical modeling methodologies that fully exploit the processing capabilities of modern massively parallel architectures in the context of a number of selected applications related to important scientific and technological challenges for the quality and the security of life in our society. At the current state of the art in technologies and methodologies, a multidisciplinary approach is required to overcome the challenges raised by the development of highly scalable numerical simulation software that can exploit computing platforms offering several hundreds of thousands of cores. Hence, the main objective of C2S@Exa is the establishment of a continuum of expertise in the computer science and numerical mathematics domains, by gathering researchers from Inria project-teams whose research and development activities are tightly linked to high performance computing issues in these domains. More precisely, this collaborative effort involves computer scientists that are experts of programming models, environments and tools for harnessing massively parallel systems, algorithmists that propose algorithms and contribute to generic libraries and core solvers in order to take benefit from all the parallelism levels with the main goal of optimal scaling on very large numbers of computing entities and, numerical mathematicians that are studying numerical schemes and scalable solvers for systems of partial differential equations in view of the simulation of very large-scale problems.