LAW: A Tool for Improved Productivity with High-Performance Linear Algebra Codes. Design and Applications
Timothy Stitt (1), Graham Kells (2), Jiri Vala (2) ((1) Irish Centre, for High-End Computing (ICHEC), Ireland, (2) National University Ireland,, Maynooth, Ireland)

TL;DR
The paper introduces LAW, a high-level Fortran 95 library that simplifies developing and modifying high-performance linear algebra codes across diverse architectures, enhancing productivity and code portability.
Contribution
LAW provides a high-level wrapper library for linear algebra kernels, abstracting low-level details and enabling single-source code development for various execution models.
Findings
LAW simplifies code development for diverse architectures.
Application of LAW in Chebyshev matrix exponentiation demonstrates its effectiveness.
LAW reduces code complexity and enhances portability in high-performance computing.
Abstract
LAPACK and ScaLAPACK are arguably the defacto standard libraries among the scientific community for solving linear algebra problems on sequential, shared-memory and distributed-memory architectures. While ease of use was a major design goal for the ScaLAPACK project; with respect to its predecessor LAPACK; it is still a non-trivial exercise to develop a new code or modify an existing LAPACK code to exploit processor grids, distributed-array descriptors and the associated distributed-memory ScaLAPACK/PBLAS routines. In this paper, we introduce what we believe will be an invaluable development tool for the scientific code developer, which exploits ad-hoc polymorphism, derived-types, optional arguments, overloaded operators and conditional compilation in Fortran 95, to provide wrappers to a subset of common linear algebra kernels. These wrappers are introduced to facilitate the abstraction…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParallel Computing and Optimization Techniques · Numerical Methods and Algorithms · Advanced Data Storage Technologies
