Memo to self:

* update version number in `stdlib/OpenBLAS_jll/Project.toml`
* update version number and sha in `deps/openblas.version`
* refresh checksums with `make -f contrib/refresh_checksums.mk -j
openblas`

In the [release notes of
v0.3.26](https://github.com/OpenMathLib/OpenBLAS/releases/tag/v0.3.26)
one of the more interesting point is perhaps

> included support for Apple M1 and newer targets in DYNAMIC_ARCH builds

Quoting from
https://github.com/JuliaPackaging/Yggdrasil/pull/7911#issuecomment-1878884892

> Some quick benchmarks on M1: with OpenBLAS 0.3.25:
> ```julia
> julia> using LinearAlgebra, BenchmarkTools
> 
> julia> peakflops()
> 1.7049262964078418e11
> 
> julia> x = randn(1_000); y = randn(size(x));
> 
> julia> @btime dot($x, $y);
>   402.705 ns (0 allocations: 0 bytes)
> ```
> with OpenBLAS v0.3.26:
> ```julia
> julia> using LinearAlgebra, BenchmarkTools
> 
> julia> peakflops()
> 1.8042546290642157e11
> 
> julia> x = randn(1_000); y = randn(size(x));
> 
> julia> @btime dot($x, $y);
>   143.777 ns (0 allocations: 0 bytes)
> ```
> Up to OpenBLAS v0.3.25 you can get similar performance on Apple
Silicon by exporting the environment variable
> ```sh
> OPENBLAS_CORETYPE=NEOVERSEN1
> ```