Théo Mary
Orcid: 0000-0001-9949-4634Affiliations:
- University of Manchester, UK
- Paul Sabatier University, Toulouse, France (PhD 2017)
According to our database1,
Théo Mary
authored at least 27 papers
between 2014 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
2014
2016
2018
2020
2022
2024
0
1
2
3
4
5
6
7
3
4
2
2
3
6
1
1
2
1
1
1
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
SIAM J. Matrix Anal. Appl., March, 2024
Communication Avoiding Block Low-Rank Parallel Multifrontal Triangular Solve with Many Right-Hand Sides.
SIAM J. Matrix Anal. Appl., March, 2024
Adaptive Precision Sparse Matrix-Vector Product and Its Application to Krylov Solvers.
SIAM J. Sci. Comput., February, 2024
Reduced-Precision and Reduced-Exponent Formats for Accelerating Adaptive Precision Sparse Matrix-Vector Product.
Proceedings of the Euro-Par 2024: Parallel Processing, 2024
Proceedings of the Euro-Par 2024: Parallel Processing, 2024
2023
ACM Commun. Comput. Algebra, June, 2023
Combining Sparse Approximate Factorizations with Mixed-precision Iterative Refinement.
ACM Trans. Math. Softw., March, 2023
Mixed precision LU factorization on GPU tensor cores: reducing data movement and memory footprint.
Int. J. High Perform. Comput. Appl., March, 2023
Matrix Multiplication in Multiword Arithmetic: Error Analysis and Application to GPU Tensor Cores.
SIAM J. Sci. Comput., February, 2023
2022
Block low-rank single precision coarse grid solvers for extreme scale multigrid methods.
Numer. Linear Algebra Appl., 2022
2021
SIAM J. Sci. Comput., 2021
Block Low-Rank Matrices with Shared Bases: Potential and Limitations of the BLR<sup>2</sup> Format.
SIAM J. Matrix Anal. Appl., 2021
2020
Sharper Probabilistic Backward Error Analysis for Basic Linear Algebra Kernels with Random Data.
SIAM J. Sci. Comput., 2020
Mixed Precision Block Fused Multiply-Add: Error Analysis and Application to GPU Tensor Cores.
SIAM J. Sci. Comput., 2020
2019
Performance and Scalability of the Block Low-Rank Multifrontal Factorization on Multicore Architectures.
ACM Trans. Math. Softw., 2019
SIAM J. Sci. Comput., 2019
Robust and Accurate Stopping Criteria for Adaptive Randomized Sampling in Matrix-Free Hierarchically Semiseparable Construction.
SIAM J. Sci. Comput., 2019
Bridging the Gap Between Flat and Hierarchical Low-Rank Matrix Formats: The Multilevel Block Low-Rank Format.
SIAM J. Sci. Comput., 2019
Improving the Complexity of Block Low-Rank Factorizations with Fast Matrix Arithmetic.
SIAM J. Matrix Anal. Appl., 2019
2018
CoRR, 2018
2017
Block Low-Rank multifrontal solvers: complexity, performance, and scalability. (Solveurs multifrontaux exploitant des blocs de rang faible: complexité, performance et parallélisme).
PhD thesis, 2017
SIAM J. Sci. Comput., 2017
2015
Performance of random sampling for computing low-rank approximations of a dense matrix on GPUs.
Proceedings of the International Conference for High Performance Computing, 2015
2014
Proceedings of the 2014 IEEE International Conference on Big Data (IEEE BigData 2014), 2014