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Pierre-Henri Tournier

dblp:183/6462 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › numerical linear algebra › linear solver
iterative linear solvers
0.212016
Block iterative methods and recycling for improved scalability of linear solvers · SC 2016
High-performance computing › numerical linear algebra
linear solver
0.212016
Block iterative methods and recycling for improved scalability of linear solvers · SC 2016
High-performance computing › sparse linear algebra
sparse linear systems
0.112016
Block iterative methods and recycling for improved scalability of linear solvers · SC 2016

Methods — techniques the papers use, named apart from their topics

recycling strategy · 0.2block iterative method · 0.2
YearPublicationVenuePosition
2019 Microwave tomographic imaging of cerebrovascular accidents by using high-performance computing
Pierre-Henri Tournier, Ioannis Aliferis, Marcella Bonazzoli, Maya de Buhan, Marion Darbas, Victorita Dolean, Frédéric Hecht, Pierre Jolivet, Ibtissam El Kanfoud, Claire Migliaccio, Frédéric Nataf, Christian Pichot, Serguei Semenov
Parallel Comput.1
2016 Block iterative methods and recycling for improved scalability of linear solvers
abstract
Contemporary large-scale Partial Differential Equation (PDE) simulations usually require the solution of large and sparse linear systems. Moreover, it is often needed to solve these linear systems with different or multiple Right-Hand Sides (RHSs). In this paper, various strategies will be presented to extend the scalability of existing multigrid or domain decomposition linear solvers using appropriate recycling strategies or block methods-i.e., by treating multiple right-hand sides simultaneously. The scalability of this work is assessed by performing simulations on up to 8,192 cores for solving linear systems arising from various physical phenomena modeled by Poisson's equation, the system of linear elasticity, or Maxwell's equation. This work is shipped as part of on open-source software, readily available and usable in any C/C++, Python, or Fortran code. In particular, some simulations are performed on top of a well-established library, PETSc, and it is shown how our approaches can be used to decrease time to solution down by 30%.
Pierre Jolivet, Pierre-Henri Tournier
SC2