Add MKL as julia 1.7 dependency + minor style cleanup

[Seelengrab]

Hi!

Julia 1.7 will feature a mechanism called "BLAS trampoline", which allows for loading a BLAS library at runtime (see here). This PR makes use of that and adds MKL as a dependency (plus project management/instantiation stuff). Someone running the benchmark doesn't need to have MKL installed already, it's installed during the activation of the julia project before the benchmarks are run. This will only work with julia 1.7+ though (which should be released relatively soon), so if that's a concern I can add a note somewhere saying that the benchmarks require it.

I've also fixed the benchmarking script, since the existing version in the repo tried to run a test_riccati.jl.in file, which didn't exist. I've also made it so that the NM and NREP parameter is passed on the command line instead of hardcoding via a file.

With this and the removal of some unnecessary (I think) copies, I get these results locally:

Note that I've only run python3 run_benchmark_julia.py as well as python3 run_benchmark_numpy.py, as I don't know how to run your BLASFEO benchmarks. I suspect the remaining difference in benchmarking times is due to this, as well as my laptop not being as fast as the machine you've run your benchmarks on. Nevertheless, since Julia+MKL can match performance of prometeo/BLASFEO once the matrix size becomes large enough i.e. we're memory bound, I suspect the speedup is real.

Cheers!

[fingolfin]

Maybe remove the semicolon(s) here, too, while you are at it?

Suggested change

	LN = RSQ[nu+1:nu+nx, nu+1:nu+nx];
	LN = RSQ[nu+1:nu+nx, nu+1:nu+nx]

[oscardssmith]

@zanellia from your perspective, is this ready to merge?

[zanellia]

Hey @Seelengrab thanks a lot for the PR and sorry for the loose response.

as well as my laptop not being as fast as the machine you've run your benchmarks on
It could also be the other way around though. Just to have a rough estimate which processor did you use for the benchmarks? Will merge anyway ;)

[oscardssmith]

IMO, the best answer would be for you to re-run the benchmark as that will ensure consistency.

[Seelengrab]

I've run this on a Intel(R) Core(TM) i7-6600U CPU @ 2.60GHz, which by now is a somewhat dated laptop CPU :)

I do agree with @oscardssmith though, a uniform rerun so you can update your graphics (julia 1.7 will be released relatively soon, as far as I know) is probably best.

[zanellia]

IMO, the best answer would be for you to re-run the benchmark as that will ensure consistency.

@oscardssmith Sure. I was not going to patch the data points together XD.

I've run this on a Intel(R) Core(TM) i7-6600U CPU @ 2.60GHz, which by now is a somewhat dated laptop CPU :)

I do agree with @oscardssmith though, a uniform rerun so you can update your graphics (julia 1.7 will be released relatively soon, as far as I know) is probably best.

@Seelengrab awesome, thanks! I will rerun everything on my laptop (FYI, back then the benchmark was run on an i7-7560U CPU running at 2.30 GHz)

[oscardssmith]

Have you run the benchmarks yet? I'm really interested in seeing how well this works when all the benchmarks are on the same machine.