Speaker
Description
Modern scientific workloads demand linear-algebra kernels that are both performant and expressive. We present the new precision-agnostic BLAS and LAPACK layer being upstreamed to the Fortran Standard Library (stdlib). The reference codes have been fully modernised and templated, delivering real and complex arithmetic in 32-, 64-, 80- and 128-bit kinds and supporting the 64-bit integer interface to scale past two-billion-element arrays and work with vendor-optimised back-ends. With one macro, 32- and 64-bit real procedures can be transparently redirected to an optimised third-party library (OpenBLAS, MKL, Accelerate, …), while stdlib continues to provide full kind-agnostic coverage.
Built on these kernels, we provide a NumPy/SciPy-style API that retains Fortran’s zero-overhead semantics: pure functions, allocation-free subroutines and intuitive operators for determinants, inverses, factorisations and other advanced solvers. A lightweight state handler offers optional, zero-cost error handling suitable for both scripting-style and HPC codes.
The talk will cover design challenges, templating strategy, one-macro integration and head-to-head performance against Python front-ends, and highlights current community contributions and roadmap items. Attendees will learn how to adopt, extend and benchmark this next-generation Fortran linear-algebra stack, positioning stdlib as an everyday alternative to established numerical platforms.
