# OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

arXiv: https://arxiv.org/abs/1609.01277 · HTML: https://mlsanity.com/paper/1609.01277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils](https://mlsanity.com/paper/2109.02183/) (2021)
- [FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers](https://mlsanity.com/paper/2607.18020/) (2026)

## Abstract (arXiv)

Exascale computing will feature novel and potentially disruptive hardware architectures. Exploiting these to their full potential is non-trivial. Numerical modelling frameworks involving finite difference methods are currently limited by the 'static' nature of the hand-coded discretisation schemes and repeatedly may have to be re-written to run efficiently on new hardware. In contrast, OpenSBLI uses code generation to derive the model's code from a high-level specification. Users focus on the equations to solve, whilst not concerning themselves with the detailed implementation. Source-to-source translation is used to tailor the code and enable its execution on a variety of hardware.