Demo Data from SIGIR 2026 / reviewers in the wild / expert
Fabrice Vidal
dblp:149/8160
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2
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 |
Parallel and multicore computing · 41% GPUs and heterogeneous computing · 26% Processor architecture and microarchitecture · 26% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
multi-GPU computing |
0.2 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
Processor architecture and microarchitecture
pipelining |
0.2 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
Parallel and multicore computing
task scheduling |
0.2 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
Parallel and multicore computing › task scheduling
dynamic scheduling |
0.1 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
Parallel and multicore computing
parallel programming runtimes |
0.1 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
High-performance computing
scientific computing systems |
0.1 | 1 | 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System · SC 2014 |
Methods — techniques the papers use, named apart from their topics
task pipelining · 0.2dynamic scheduling · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | High Performance Pseudo-analytical Simulation of Multi-Object Adaptive Optics over Multi-GPU Systems
Ahmad Abdelfattah, Eric Gendron, Damien Gratadour, David E. Keyes, Hatem Ltaief, Arnaud Sevin, Fabrice Vidal |
Euro-Par | 7 |
| 2014 | Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU SystemabstractThe European Extremely Large Telescope project (E-ELT) is one of Europe's highest priorities in ground-based astronomy. ELTs are built on top of a variety of highly sensitive and critical astronomical instruments. In particular, a new instrument called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used to drive the deformable mirror in real time from the measurements. A new numerical algorithm is proposed (1) to capture the actual experimental noise and (2) to substantially speed up previous implementations by exposing more concurrency, while reducing the number of floating-point operations. Based on the Matrices Over Runtime System at Exascale numerical library (MORSE), a dynamic scheduler drives all computational stages of the tomographic reconstruct or simulation and allows to pipeline and to run tasks out-of order across different stages on heterogeneous systems, while ensuring data coherency and dependencies. The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup. At more than 50000 unknowns, this appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations. Ali Charara 0001, Hatem Ltaief, Damien Gratadour, David E. Keyes, Arnaud Sevin, Ahmad Abdelfattah, Eric Gendron, Carine Morel, Fabrice Vidal |
SC | 9 |