66 citations · 93 across the 7 of their papers we have counts for
8 papers
An Efficient Batch Solver for the Singular Value Decomposition on GPUs
Ahmad Abdelfattah, Massimiliano Fasi
The singular value decomposition (SVD) is a powerful tool in modern numerical linear algebra, which underpins computational methods such as principal component analysis (PCA), low-…
Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic
Ahmad Abdelfattah, Jack Dongarra, Massimiliano Fasi +2
Ootomo, Ozaki, and Yokota [Int. J. High Perform. Comput. Appl., 38 (2024), p. 297-313] have proposed a strategy to recast a floating-point matrix multiplication in terms of integer…
Interface for Sparse Linear Algebra Operations
Ahmad Abdelfattah, Willow Ahrens, Hartwig Anzt +32
The standardization of an interface for dense linear algebra operations in the BLAS standard has enabled interoperability between different linear algebra libraries, thereby boosti…
GPU-Based Homotopy Continuation for Minimal Problems in Computer Vision
Chiang-Heng Chien, Hongyi Fan, Ahmad Abdelfattah +3
Systems of polynomial equations arise frequently in computer vision, especially in multiview geometry problems. Traditional methods for solving these systems typically aim to elimi…
GPU Algorithms for Efficient Exascale Discretizations
Ahmad Abdelfattah, Valeria Barra, Natalie Beams +24
In this paper we describe the research and development activities in the Center for Efficient Exascale Discretization within the US Exascale Computing Project, targeting state-of-t…
Efficient Exascale Discretizations: High-Order Finite Element Methods
Tzanio Kolev, Paul Fischer, Misun Min +27
Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that ex…
A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic
Ahmad Abdelfattah, Hartwig Anzt, Erik G. Boman +22
Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the Machine Learning community and their demand for…
KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators
Ahmad Abdelfattah, David Keyes, Hatem Ltaief
KBLAS is a new open source high performance library that provides optimized kernels for a subset of Level 2 BLAS functionalities on CUDA-enabled GPUs. Since performance of dense ma…