16 citations · 44 across the 9 of their papers we have counts for
10 papers
MOM: Matrix Operations in MLIR
Lorenzo Chelini, Henrik Barthels, Paolo Bientinesi +3
Modern research in code generators for dense linear algebra computations has shown the ability to produce optimized code with a performance which compares and often exceeds the one…
Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs
Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels +2
Achieving high-performance GPU kernels requires optimizing algorithm implementations to the targeted GPU architecture. It is of utmost importance to fully use the compute and memor…
Linnea: Automatic Generation of Efficient Linear Algebra Programs
Henrik Barthels, Christos Psarras, Paolo Bientinesi
The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance c…
The Linear Algebra Mapping Problem. Current state of linear algebra languages and libraries
Christos Psarras, Henrik Barthels, Paolo Bientinesi
We observe a disconnect between the developers and the end users of linear algebra libraries. On the one hand, the numerical linear algebra and the high-performance communities inv…
Automatic Generation of Efficient Linear Algebra Programs
Henrik Barthels, Christos Psarras, Paolo Bientinesi
The level of abstraction at which application experts reason about linear algebra computations and the level of abstraction used by developers of high-performance numerical linear…
The Generalized Matrix Chain Algorithm
Henrik Barthels, Marcin Copik, Paolo Bientinesi
In this paper, we present a generalized version of the matrix chain algorithm to generate efficient code for linear algebra problems, a task for which human experts often invest da…
MatchPy: A Pattern Matching Library
Manuel Krebber, Henrik Barthels, Paolo Bientinesi
Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstra…
Efficient Pattern Matching in Python
Manuel Krebber, Henrik Barthels, Paolo Bientinesi
Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax tre…
Systematic Generation of Algorithms for Iterative Methods
Henrik Barthels
The FLAME methodology makes it possible to derive provably correct algorithms from a formal description of a linear algebra problem. So far, the methodology has been successfully u…
The Matrix Chain Algorithm to Compile Linear Algebra Expressions
Henrik Barthels, Paolo Bientinesi
The matrix chain problem consists in finding the parenthesization of a matrix product that minimizes the number of scalar operations. In practical applica…