Section: Partnerships and Cooperations

National Initiatives

ANR

ANR SATAS SAT as a Service (http://www.agence-nationale-recherche.fr/Project-ANR-15-CE40-0017).

• 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.

ANR DASH Data-Aware Scheduling at Higher scale (https://project.inria.fr/dash/).

• AP générique JCJC 2017, 03/2018 - 02/2022 (48 months)

• Coordinator: Guillaume Pallez (Tadaam)

• Abstract: This project focuses on the effecient execution of I/O for High-Performance applications. The idea is to take into account some knowledge on the behavior of the different I/O steps to compute efficient schedules, and to update them dynamically with the online information.

ANR Solharis SOLvers for Heterogeneous Architectures over Runtime systems, Investigating Scalability .

• AAPG ANR 2019, 2019 - 2023 (48 months)

• Coordinator: Alfredo Buttari (IRIT-INPT)

• Abstract: The Solharis project aims at producing scalable methods for the solution of large sparse linear systems on large heterogeneous supercomputers, using the StarPU runtime system, and to address the scalability issues both in runtime systems and in solvers.

ADT - Inria Technological Development Actions

ADT Gordon

• 10/2018 - 09/2020 (24 months)

• Coordinator: Emmanuel Jeannot

• Other partners: Storm, HiePACS, PLEIADE (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.

IPL - Inria Project Lab

High-Performance computing and BigData

• Participants: Guillaume Pallez, Emmanuel Jeannot, Nicolas Vidal, Francieli Zanon-Boito

• HPC and Big Data evolved with their own infrastructures (supercomputers versus clouds), applications (scientific simulations versus data analytics) and software tools (MPI and OpenMP versus Map/Reduce or Deep Learning frameworks). But Big Data analytics is becoming more compute-intensive (thanks to deep learning), while data handling is becoming a major concern for scientific computing. 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

Collaboration with CERFACS

Developments on the Hippo software

• Participants: Brice Goglin, Guillaume Mercier

• A Memorandum of Understanding is currently being negociated between Inria and CERFACS to organize the collaboration between both entities pertaining to the developements on the Hippo software. The goal is to provide a portable solution to address the issue of dynamic placement of hybrid coupled MPI + OpenMP applications, especially for climate modelling. Météo France is one of the targer of this work but other teams/institutes around the globe have expressed an interest in Hippo . Therefore we want to create a solution that would match the needs of the community on the whole.