1 citations · 2 across the 4 of their papers we have counts for
7 papers
Communication Lower Bounds and Optimal Algorithms for Symmetric Matrix Computations
Hussam Al Daas, Grey Ballard, Laura Grigori +3
In this article, we focus on the communication costs of three symmetric matrix computations: i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) i…
Tight Memory-Independent Parallel Matrix Multiplication Communication Lower Bounds
Hussam Al Daas, Grey Ballard, Laura Grigori +2
Communication lower bounds have long been established for matrix multiplication algorithms. However, most methods of asymptotic analysis have either ignored the constant factors or…
Efficient Algebraic Two-Level Schwarz Preconditioner For Sparse Matrices
Hussam Al Daas, Pierre Jolivet, Tyrone Rees
Domain decomposition methods are among the most efficient for solving sparse linear systems of equations. Their effectiveness relies on a judiciously chosen coarse space. Originall…
Randomized algorithms for rounding in the Tensor-Train format
Hussam Al Daas, Grey Ballard, Paul Cazeaux +5
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions o…
A Robust Algebraic Multilevel Domain Decomposition Preconditioner For Sparse Symmetric Positive Definite Matrices
Hussam Al Daas, Pierre Jolivet
Domain decomposition (DD) methods are widely used as preconditioner techniques. Their effectiveness relies on the choice of a locally constructed coarse space. Thus far, this const…
Parallel Algorithms for Tensor Train Arithmetic
Hussam Al Daas, Grey Ballard, Peter Benner
We present efficient and scalable parallel algorithms for performing mathematical operations for low-rank tensors represented in the tensor train (TT) format. We consider algorithm…