Section: Other Grants and Activities
Plasma: modeling of magnetized plasmas
Grant: ARC INRIA
Partners: CALVI (INRIA LORIA - leader of the project), MC2 (INRIA Futurs Bordeaux), SIMPAF (INRIA Futurs Lille), MIP (Toulouse), CEA Caradarache
Overview: The description of magnetized plasmas uses a hierarchy of models; this leads to several open problems: modeling and role of adimensionnalised parameters, mathematical analysis of the models and their asymptotic behavior when some parameters tends to infinity, numerical simulation of these (simplified) models. The role of this ARC is to cover this range of problems, from the analysis to the numerical simulation.
COA: a computational steering environment for distributed numerical simulations
Grant: ARC INRIA
Partners:PARIS (INRIA Rennes), Projet JACQUARD (INRIA Futurs Lille)
Overview: This 2-year project is funded by the INRIA Cooperative Research Initiative (ARC) whose partners are the PARIS, Jacquard and ScAlApplix Project-Teams. Its objective is to design an experimental platform allowing dynamic adaptation and steering of distributed numerical simulation applications using aspect weaving technics on top of component models.
Sire: computing simulation of enzymatic systems: from structural to functional aspects
Participant : Olivier Coulaud.
Grant: ACI IMPIO (``Action Concertée Incitative Informatique, Mathématiques, Physique en Biologie Moléculaire'' – French Ministry of Research)
Dates: 2004 – 2006
Partners: CBT and MAEM (UHP Nancy 1, CNRS)
Overview: The goal of this action is to study the using of linear scaling algorithms in order to understand the behavior of Methionine synthase reductase enzymes.
MASSIM: development of a software environment for interactively processing and visualizing complex large scale data coming from numerical simulations
Grant: ANR MMSA - ARA MAsses de données
Dates: 2005 – 2008
Partners: IRMA (Strasbourg, UMR 7001)), LSIIT (leader of the project, Strasbourg, UMR 7005)
Overview: Numerical simulation is a continuously growing area, especially with the increasing computational power of current computer technology, thus covering larger and larger scientific application fields. But at these days, monitoring tools are still seriously lacking, since developers and users desire more and more to get faster and faster feedbacks of the simulation results. In this project, we are interested in large scale simulations dealing with complex data (multivariate and multidimensional). Our aim is to realize a plate-form / framework to couple parallel and distributed simulations, like in GRID'5000, with an interactive monitoring and visualization system. This plate-form will be validated on two types of large scale applications: plasma and fracture propagation simulation using multi-scale approaches. For these applications, the simulation codes are definitely very complex and need some highly efficient tools to represent the large amount of data, to redistribute the data using visualization and to control and validate the corresponding computation algorithms. Since, results may be multivariate and multidimensional, they need also specific data exploration and visualization tools.
NUMASIS: adaptation and optimization of the applicatives performances on NUMA architectures
Dates: 2006 – 2009
Partners: Bull, Total, BRGM, CEA, ID-Imag (leader of the project), PARIS (IRISA), Runtime (INRIA Futurs Bordeaux).
Overview: The multiprocessor machines of tomorrow will rely on an NUMA architecture introducing multiple levels of hierarchy into computers (multi-modules, chips multi-body, multithreading material, etc). To exploit these architectures, parallel applications must use powerful runtime supports making possible the distribution of execution and data streams without compromising their portability. Project NUMASIS proposes to evaluate the functionalities provided by the current systems, to apprehend the limitations, to design and implement new mechanisms of management of the processes, data and communications within the basic softwares (operating system, middleware, libraries). The target algorithmic tools that we retained are parallel linear sparse solvers with application to seismology.