A
Ali Charara
Researcher at King Abdullah University of Science and Technology
Publications - 20
Citations - 240
Ali Charara is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Linear algebra & Matrix (mathematics). The author has an hindex of 9, co-authored 20 publications receiving 174 citations. Previous affiliations of Ali Charara include University of Tennessee.
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Proceedings ArticleDOI
SLATE: design of a modern distributed and accelerated linear algebra library
TL;DR: The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU-GPU based and CPU based.
Book ChapterDOI
Exploiting Data Sparsity for Large-Scale Matrix Computations
TL;DR: The Hierarchical matrix Computations on Manycore Architectures (HiCMA) library is extended to provide a high-performance implementation on distributed-memory systems of one of the most widely used matrix factorization in large-scale scientific applications, i.e., the Cholesky factorization.
Proceedings ArticleDOI
A novel fast and accurate pseudo-analytical simulation approach for MOAO
Eric Gendron,Ali Charara,Ahmad Abdelfattah,Damien Gratadour,David E. Keyes,Hatem Ltaief,Carine Morel,Fabrice Vidal,Arnaud Sevin,Gérard Rousset +9 more
TL;DR: A novel hybrid, pseudo-analytical simulation scheme that allows to simulate in detail the tomographic problem as well as noise and aliasing with a high fidelity, and opens the way for a future on-sky implementation of the tomography control, plus the joint PSF and performance estimation.
Journal ArticleDOI
Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs
TL;DR: This work describes the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes (up to 256) using single and multiple GPUs and outperforms existing state-of-the-art implementations.
Proceedings ArticleDOI
Pipelining computational stages of the tomographic reconstructor for multi-object adaptive optics on a multi-GPU system
Ali Charara,Hatem Ltaief,Damien Gratadour,David E. Keyes,Arnaud Sevin,Ahmad Abdelfattah,Eric Gendron,Carine Morel,Fabrice Vidal +8 more
TL;DR: The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup, and appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations.