Section: Partnerships and Cooperations

National Initiatives

ANR

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 Scheduling 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 Sud-Ouest, Bull/ATOS

• 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

ANR SATAS SAT as a Service.

• AP générique 2015, 01/2016 - 12-2019 (48 months)

• Coordinator: Laurent Simon (LaBRI)

• Other partners: CRIL (Univ. Artois), Inria Lille (Spirals)

• Abstract: The SATAS project aims to advance the state of the art in massively parallel SAT solving. The final goal of the project is to provide a “pay as you go” interface to SAT solving services and will extend the reach of SAT solving technologies, daily used in many critical and industrial applications, to new application areas, which were previously considered too hard, and lower the cost of deploying massively parallel SAT solvers on the cloud.

IPL - Inria Project Lab

MULTICORE - Large scale multicore virtualization for performance scaling and portability

• Participants: Emmanuel Jeannot.

• Multicore processors are becoming the norm in most computing systems. However supporting them in an efficient way is still a scientific challenge. This large-scale initiative introduces a novel approach based on virtualization and dynamicity, in order to mask hardware heterogeneity, and to let performance scale with the number and nature of cores. It aims to build collaborative virtualization mechanisms that achieve essential tasks related to parallel execution and data management. We want to unify the analysis and transformation processes of programs and accompanying data into one unique virtual machine. We hope delivering a solution for compute-intensive applications running on general-purpose standard computers.