To share the details, here's a post demonstrating Tullio.jl, a Julia-based higher order tensor library, is faster than OpenBLAS for gemm kernels: https://disco

To share the details, here's a post demonstrating Tullio.jl, a Julia-based higher order tensor library, is faster than OpenBLAS for gemm kernels: https://discourse.julialang.org/t/realistically-how-close-is-gaius-jl-to-becoming-a-full-replacement-for-blas-in-julia/42780/5 https://discourse.julialang.org/t/realistically-how-close-is... And PaddedMatrices.jl is another example: https://discourse.julialang.org/t/ann-paddedmatrices-jl-julia-blas-and-partially-sized-arrays/38215 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.