Show HN: LinearSolveBench, interesting new benchmark to discover linear solvers (www.autodidakt.ai)

A new benchmark tool named LinearSolveBench has been introduced to enhance the discovery and evaluation of linear solvers, particularly for large sparse linear systems crucial in various scientific and engineering fields. The benchmark utilizes linear systems derived from FLASH magnetic diffusion scenarios, featuring matrices with a range of sizes and sparsity. Notable AI models, such as Fable and GPT, have been tested on this benchmark, showcasing significant speedups against traditional solvers like GMRES combined with BoomerAMG. For instance, Fable 5.1 achieved a remarkable 1.546× speedup, which highlights the potential of AI in efficiently handling complex linear equations. This advancement is significant for the AI/ML community as it aims to catalyze algorithmic innovations in solving large-scale linear problems, which are foundational in areas like power grid optimization, quantitative finance, structural mechanics, and fluid dynamics. By leveraging large language models to aid in algorithm discovery, LinearSolveBench not only streamlines the evaluation process but also promotes collaboration between computational intelligence and traditional mathematical approaches. As the demand for faster and more efficient solutions grows, this benchmark could lead to breakthroughs that significantly improve computational capabilities across diverse applications.