Armadillo: an Efficient Framework for Numerical Linear Algebra

A major challenge in the deployment of scientific software solutions is the adaptation of research prototypes to production-grade code. While high-level languages like MATLAB are useful for rapid prototyping, they lack the resource efficiency required for scalable production applications, necessitat...

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Bibliographic Details
Published inProceedings (International Conference on Computer and Automation Engineering. Online) pp. 303 - 307
Main Authors Sanderson, Conrad, Curtin, Ryan
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.03.2025
Subjects
automated mapping
BLAS
C++ languages
expression optimisation
LAPACK
Libraries
Linear algebra
Machine learning
Manuals
Memory management
metaprogramming
numerical linear algebra
Optimization
Production
Signal processing
Sparse matrices
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ISSN2154-4360
DOI10.1109/ICCAE64891.2025.10980539

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Summary:A major challenge in the deployment of scientific software solutions is the adaptation of research prototypes to production-grade code. While high-level languages like MATLAB are useful for rapid prototyping, they lack the resource efficiency required for scalable production applications, necessitating translation into lower level languages like C++. Further, for machine learning and signal processing applications, the underlying linear algebra primitives, generally provided by the standard BLAS and LAPACK libraries, are unwieldy and difficult to use, requiring manual memory management and other tedium. To address this challenge, the Armadillo C++ linear algebra library provides an intuitive interface for writing linear algebra expressions that are easily compiled into efficient production-grade implementations. We describe the expression optimisations we have implemented in Armadillo, exploiting template metaprogramming. We demonstrate that these optimisations result in considerable efficiency gains on a variety of benchmark linear algebra expressions.
ISSN:2154-4360
DOI:10.1109/ICCAE64891.2025.10980539