| Version |
Date |
Utilisateur |
ID du Champ |
Champ |
Difference |
| 6 |
|
|
183 |
Objectives |
| - | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, our objective is that the software developed during this thesis would be part of a reference public domain library for fast HPC solvers. |
+ | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, the software developed during this thesis would be part of a reference public domain library for fast HPC solvers. |
|
| |
|
|
225 |
Domain - extra |
| - | algorithms and software for HPC |
+ | HPC |
|
| 5 |
|
|
183 |
Objectives |
| - | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be in applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, our objective is that the software developed during this thesis would be part of a reference public domain library for fast HPC solvers. |
+ | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, our objective is that the software developed during this thesis would be part of a reference public domain library for fast HPC solvers. |
|
| |
|
|
225 |
Domain - extra |
| - | HPC |
+ | algorithms and software for HPC |
|
| 3 |
|
|
183 |
Objectives |
| - | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be in applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, one of our aim is that the software developed during this thesis would be part of a reference public domain library. |
+ | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be in applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, our objective is that the software developed during this thesis would be part of a reference public domain library for fast HPC solvers. |
|
| 2 |
|
|
183 |
Objectives |
| - | |
+ | This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be in applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, one of our aim is that the software developed during this thesis would be part of a reference public domain library. |
|
| |
|
|
189 |
Collaborations |
| - | |
+ | University of Tennessee (Knoxville, USA), Lawrence Berkeley National Laboratory (Berkeley, USA) |
|
| 1 |
|
|
181 |
Context |
| - | |
+ | This PhD thesis will take advantage of the new associate-team R-LAS with University of Tennessee (project lead by Marc Baboulin and Jack Dongarra). In the framework of this project, the PhD candidate will benefit from extended visits to Innovative Computing Laboratory (Knoxville, Tennessee), which is one of the world leader in High-Performance Computing. |
|
| |
|
|
225 |
Domain - extra |
|