arXiv subjects

Margaret E. Myers

Publications and source records attributed to Margaret E. Myers.

3 recordsLinked to original sources

arXiv2026

Enabling Pivoting in the Formal Derivation of LU factorization

The FLAME methodology for deriving linear algebra algorithms from specification, first introduced around 2000, has been successfully applied to a broad cross section of operations. An open question has been whether it can yield algorithms for the best-known operation in linear algebra, LU factorization with partial pivoting (Gaussian elimination with row swapping). This paper shows that it can and provides general techniques for pivoted factorizations.

cs.MS↗

arXiv2026

A Proposed Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES)

We leverage highly successful prior projects sponsored by multiple NSF grants and gifts from industry: the BLAS-like Library Instantiation Software (BLIS) and the libflame efforts to lay the foundation for a new flexible framework by vertically integrating the dense linear and multi-linear (tensor) software stacks that are important to modern computing. This vertical integration will enable high-performance computations from node-level to massively-parallel, and across both CPU and GPU architectures. The effort builds on decades of experience by the research team turning fundamental research on the systematic derivation of algorithms (the NSF-sponsored FLAME project) into practical software for this domain, targeting single and multi-core (BLIS, TBLIS, and libflame), GPU-accelerated (SuperMatrix), and massively parallel (PLAPACK, Elemental, and ROTE) compute environments. This project will implement key linear algebra and tensor operations which highlight the flexibility and effectiveness of the new framework, and set the stage for further work in broadening functionality and integration into diverse scientific and machine learning software.

cs.MS↗

arXiv2018

A Simple Methodology for Computing Families of Algorithms

Discovering "good" algorithms for an operation is often considered an art best left to experts. What if there is a simple methodology, an algorithm, for systematically deriving a family of algorithms as well as their cost analyses, so that the best algorithm can be chosen? We discuss such an approach for deriving loop-based algorithms. The example used to illustrate this methodology, evaluation of a polynomial, is itself simple yet the best algorithm that results is surprising to a non-expert: Horner's rule. We finish by discussing recent advances that make this approach highly practical for the domain of high-performance linear algebra software libraries.

cs.PL↗