So there isn't just one example but multiples at this point. RecursiveFactorization's `lu` tends to perform better than MKL for small matrices and this is what we use in the differential equation solvers when there are <100 ODEs.
That's all on CPUs though. KernelAbstractions.jl is the GPU version and that's still being constructed but moving strong.
https://discourse.julialang.org/t/realistically-how-close-is...
And PaddedMatrices.jl is another example:
https://discourse.julialang.org/t/ann-paddedmatrices-jl-juli...
So there isn't just one example but multiples at this point. RecursiveFactorization's `lu` tends to perform better than MKL for small matrices and this is what we use in the differential equation solvers when there are <100 ODEs.
That's all on CPUs though. KernelAbstractions.jl is the GPU version and that's still being constructed but moving strong.