Main.HomePage History
Hide minor edits - Show changes to markup
[+CEA-EDF-INRIA school\\
Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems+]
CEA-EDF-INRIA school\\
Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school\\ Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
[+CEA-EDF-INRIA school\\
Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems+]
CEA-EDF-INRIA school - Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school\\ Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school on robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school - Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html | Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems]
CEA-EDF-INRIA school on robust methods and algorithms for solving large algebraic systems on modern high performance computing systems
CEA-EDF-INRIA school Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
CEA-EDF-INRIA school [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html | Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems]
CEA-EDF-INRIA school organized by the PhyLeaS partners at the [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
CEA-EDF-INRIA school Robust methods and algorithms for solving large algebraic systems on modern high performance computing systems [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
CEA-EDF-INRIA school organized by the PhyLeaS partners at the [[ http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
CEA-EDF-INRIA school organized by the PhyLeaS partners at the [[http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
CEA-EDF-INRIA school organized by the PhyLeaS partners at the [[ http://www.inria.fr/actualites/colloques/cea-edf-inria/2009/calculhp/index.en.html| ]
Mini-symposium organized by the PhyLeaS partners at SIAM Conference on Computational Science and Engineering (CSE09)
Mini-symposium organized by the PhyLeaS partners at the SIAM Conference on Computational Science and Engineering (CSE09)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
Mini-symposium organized by the PhyLeaS partners at SIAM Conference on Computational Science and Engineering (CSE09)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
Mini-symposium organized by the PhyLeaS partners at SIAM Conference on Computational Science and Engineering (CSE09)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
Mini-symposium organized by the PhyLeaS partners at SIAM Conference on Computational Science and Engineering (CSE09)
(:table border='0' width='80%' cellspacing='10px':)
(:table border='0' width='100%' cellspacing='10px':)
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
(:table border='0' width='80%' cellspacing='10px':) (:cellnr align='center':) http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg (:tableend:)
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
http://www-sop.inria.fr/nachos/phyleas/docs/5-18July2008/group_meeting.jpg
Welcome to the PhyLeaS associate team home page.
Welcome to the PhyLeaS associate team home page!
PhyLeaS is funded by the INRIA "Associate Team" programme (starting date: January 2008)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
PhyLeaS is funded by the INRIA "Associate Team" programme (starting date: January 2008)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
PhyLeaS is funded by the INRIA "Associate Team" programme (starting date: January 2008)
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
INRIA Bordeaux - Sud-Ouest research center, ScAlApplix project-team and LaBRI UMR CNRS 5800. \\
INRIA Bordeaux - Sud-Ouest research center, SCALAPPLIX project-team and LaBRI UMR CNRS 5800. \\
Team leader: Jean Roman\\
Team leader: Jean Roman\\
Team leader: Jean Roman
INRIA Bordeaux - Sud-Ouest research center, ScAlApplix project-team and LaBRI UMR CNRS 5800.
Team leader: Jean Roman
INRIA Bordeaux - Sud-Ouest research center, ScAlApplix project-team and LaBRI UMR CNRS 5800. \\
Team leader: Jean Roman\\
Team leader: Jean Roman\\
Team leader: Jean Roman
INRIA Bordeaux - Sud-Ouest research center, ScAlApplix project-team and LaBRI UMR CNRS 5800.
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The aim of this project is the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The research activities undertaken in the framework of the PhyLeaS associate team aim at the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
Welcome to the PhyLeaS associate team home page.
Welcome to the PhyLeaS associate team home page.
New advances in high performance scientific computing require continuing the development of innovative algorithmic and numerical techniques, their efficient implementation on modern massively parallel computing platforms and their integration in application software in order to perform large-scale numerical simulations currently out of reach. The solution of sparse linear systems is a basic kernel which appears in many academic and industrial applications based on partial differential equations (PDEs) modeling physical phenomena of various nature. In most of the applications, this basic kernel is used many times (numerical optimization procedure, implicit time integration scheme, etc.) and often accounts for the larger part of the computing time. In a competitive environment where the numerical simulation tends to replace the experiment, the modeling calls for PDEs of ever increasing complexity. Furthermore, realistic applications involve multiple space and time scales, and non-trivial geometrical features. In this context, a common trend is to discretize the underlying PDE models using arbitrary high-order finite element methods designed on unstructured grids. As a consequence, the resulting algebraic systems are irregularly structured and very large in size. The aim of this project is the design and efficient implementation of parallel hybrid linear system solvers which combine the robustness of direct methods with the implementation flexibility of iterative schemes. These approaches are candidate to get scalable solvers on massively parallel computers.
Welcome to the PhyLeaS associated team home page
Welcome to the PhyLeaS associate team home page.
Welcome to PmWiki!
A local copy of PmWiki's documentation has been installed along with the software, and is available via the documentation index.
To continue setting up PmWiki, see initial setup tasks.
The basic editing page describes how to create pages in PmWiki. You can practice editing in the wiki sandbox.
More information about PmWiki is available from http://www.pmwiki.org .
Welcome to the PhyLeaS associated team home page