Add support for Float32

[sshin23]

This PR adds support for Float32, or any other precision types.

To make this possible, we make the AbstractLinearSovler a parametric type, where the precision is given as a type parameter. The Float32 version of the solver interfaces is added when it is available.

[frapac]

That's a significant improvement in MadNLP! I read this PR and had only a few minor comments so far. It would be interesting to find a good use-case to advertise this new capability. Maybe we should run the GPU benchmark on a simple GPU that does not support double precision?

[sshin23]

Thanks, @frapac for the review! Indeed creating a good use case would be important, and probably it doesn't have a big advantage on CPU.

Just ran a simple experiment on my laptop:

julia> T=Float32; N=400; a = CUDA.randn(T,N,N); a = a*a'+I; @time cholesky(a);
  0.000969 seconds (163 allocations: 9.016 KiB)

julia> T=Float64; N=400; a = CUDA.randn(T,N,N); a = a*a'+I; @time cholesky(a);
  0.002487 seconds (163 allocations: 9.016 KiB)

so, would be interesting to test the performance with a very large-scale dense problem on GPU. Would be interesting to test it with DynamicNLPModels.jl

cc: @dlcole3