Hussam is a researcher in the Computational Mathematics Group (CMG) at STFC Rutherford Appleton Laboratory and a Visiting Research Fellow in the Numerical Analysis Group at the University of Oxford. Hussam received his masters in fundamental and applied mathematics from Paris-sud University in 2014. He received a PhD in Numerical Analysis at Inria-Paris and Sorbonne University (Paris-VI) in 2018. Afterwards, he spent 18 months as a postdoctoral fellow in the Computational Methods in Control and System Theory Group in the Max Planck Institute in Magdeburg, Germany. Starting from August 2020, he joined CMG.
His research interests lie in the fields of numerical linear and multilinear algebra and high performance computing. In particular, he is interested in solving large-scale sparse linear systems using preconditioned (accelerated) iterative methods focusing on acceleration techniques that are algebraic (no dependency on the underlying problem), robust with respect to problems parameters, and scalable.
08-01-2024: With P. Jolivet, F. Nataf, and P.-H. Tournier, we submitted a journal preprint: A Robust Two-Level Schwarz Preconditioner for Sparse Matrices
13-08-2023: I gave a talk at the Numerical Analysis in the 21st Century Conference
1- A Robust Two-Level Schwarz Preconditioner For Sparse Matrices. 2024. preprint
H. Al Daas, P. Jolivet, F. Nataf, P.-H. Tournier
14- Communication Lower Bounds and Optimal Algorithms for Multiple Tensor-Times-Matrix
Computation.
SIAM J. Matrix Anal. Appl., 45(1), 450--477. 2024. Paper, preprint
H. Al Daas, G. Ballard, L. Grigori, S. Kumar, K. Rouse
13- Parallel Memory-Independent Communication Bounds for SYRK.
SPAA 2023 Proceedings. Paper, Preprint.
H. Al Daas, G. Ballard, L. Grigori, S. Kumar, K. Rouse
12- Efficient Algebraic Two-Level Schwarz Preconditioner For Sparse Matrices.
SIAM J. Sci. Comput., 45(3), A1199--A1213. 2023. Paper, preprint
H. Al Daas, P. Jolivet, T. Rees
11- Randomized algorithms for rounding in the Tensor-Train format.
SIAM J. Sci. Comput. 45(1), A74--A95. 2023. Paper.
H. Al Daas, G. Ballard, P. Cazeaux, E. Hallman, A. Miedlar, M. Pasha, T. W. Reid, A. K.
Saibaba
Matlab code for reproducing the results available here.
10- A Robust Algebraic Multilevel Domain Decomposition Preconditioner For Sparse Symmetric
Positive Definite Matrices.
SIAM J. Sci. Comput., 44(4), A2582--A2598. 2022. Paper preprint
H. Al Daas, P. Jolivet
9- Brief Announcement: Tight Memory-Independent Parallel Matrix Multiplication Communication
Lower Bounds.
SPAA 2022 Proceedings. Paper.
H. Al Daas, G. Ballard, L. Grigori, S. Kumar, K. Rouse
8- Parallel Tensor Train Rounding using Gram SVD.
IPDPS 2022 Proceedings. Paper .
H. Al Daas, G. Ballard, L. Maning
7- A Robust Algebraic Domain Decomposition Preconditioner for Sparse Normal
Equations.
SIAM J. Sci. Comput., 44(3), A1047--A1068. 2022. journal, preprint.
H. Al Daas, P. Jolivet, J. Scott
Code for reproducing the results available here, PETSc interface through the
HPDDM package, PCHPDDM.
6- Parallel Algorithms for Tensor Train Arithmetic.
SIAM J. Sci. Comput., 44(1), C25--C53. 2022. journal, preprint
H. Al Daas, G. Ballard, P. Benner
MPI Algorithms for Tensor-Train Arithmetic (MPI_ATTAC) is a library implementing algorithms in
the paper. It is available here.
5- Two-level Nyström--Schur preconditioner for sparse symmetric positive definite
matries
SIAM J. Sci. Comput., 43(6), A3837--A3861. 2021. journal, preprint
H. Al Daas, T. Rees, J. Scott
Code for reproducing numerical experiments available here.
4- A Multilevel Schwarz Preconditioner Based on a Hierarchy of Robust Coarse Spaces.
SIAM J. Sci. Comput., 43(3), A1907--A1928. 2021. journal, preprint
H. Al Daas, L. Grigori, P. Jolivet, P. H. Tournier
Code for reproducing the results available here, PETSc interface through the
HPDDM package, PCHPDDM.
3- Recycling Krylov Subspaces and Truncating Deflation Subspaces for Solving Sequence of
Linear Systems.
ACM Transactions on Mathematical Software Volume 47 Issue 2 June 2021 Article No.: 13
pp 1--30. journal, preprint.
H. Al Daas, L. Grigori, P. Henon, P. Ricoux
2- A Class of Efficient Locally Constructed Preconditioners Based on Coarse Spaces.
SIAM J. Matrix Anal. Appl., 40(1), 66--91. 2019. journal, preprint.
H. Al Daas, L. Grigori
1- Enlarged GMRES for solving linear systems with one or multiple right-hand sides.
IMA Journal of Numerical Analysis, Vol. 39, 2019, pp. 1924--1956. 2019. journal.
H. Al Daas, L. Grigori, P. Henon, P. Ricoux
3- Tight Memory-Independent Parallel Matrix Multiplication Communication Lower Bounds.
2022. preprint
H. Al Daas, G. Ballard, L. Grigori, S. Kumar, K. Rouse
2- An extended Krylov-like method for the solution of multi-linear systems. 2021. preprint
H. Al Daas, D. Lombardi
1- Low-Rank and Total Variation Regularization and Its Application to Image Recovery.
2020. preprint.
P. Goyal, H. Al Daas, P. Benner
A Robust Two-Level Schwarz Preconditioner For Sparse Matrices.
PETSc interface through the HPDDM package, PCHPDDM.
Efficient Algebraic Two-Level Schwarz Preconditioner For Sparse Matrices.
PETSc interface through the HPDDM package, PCHPDDM.
A Robust Algebraic Multilevel Domain Decomposition Preconditioner For Sparse Symmetric
Positive Definite Matrices.
PETSc interface through the HPDDM package, PCHPDDM.
A Robust Algebraic Domain Decomposition Preconditioner for Sparse Normal Equations.
Code for reproducing the results available here.
Soon: PETSc interface through the HPDDM package, PCHPDDM.
Two-level Nyström--Schur preconditioner for sparse symmetric positive definite
matries
Code for reproducing numerical experiments available here.
A Multilevel Schwarz Preconditioner Based on a Hierarchy of Robust Coarse Spaces.
Code for reproducing the results available here.
PETSc interface through the HPDDM package, PCHPDDM.
HSL_MP82 .
Parallel orthonormalization procedures (QR and SVD) for tall-skinny matrices: TSQR, CholQR,
CholQR2, CholQR3 and Gram-based SVD here.
This is part of the Harwell Subroutine Library
(HSL).
Randomized algorithms for rounding in the Tensor-Train format.
Matlab code for reproducing the results available here.
MPI_ATTAC Parallel Algorithms for Tensor Train Arithmetic.
MPI Algorithms for Tensor-Train Arithmetic (MPI_ATTAC) is a library implementing
algorithms in the paper. It is available here.
Technical Paper Committee Member of the Algorithms Area in Super Computing 2024.
2022-2024 SIAM UKIE Secretary and Treasurer .
Technical Paper Committee Member of Area 1: Applications, Algorithms, and Libraries in IEEE Cluster 2022.
Co-organizer of the Online series Communication in Numerical Linear Algebra.
Programm Committee Member of TOPIC 10: PARALLEL NUMERICAL METHODS AND APPLICATIONS in EURO-PAR 2020.