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Some suggestions but otherwise looks great.
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@kshyatt I think this one is good to go, only CUDA seems to somehow go out of memory and I'm confused why this is only now happening. I reduced the sizes a bit, but if this doesn't end up helping I might just call this unrelated to this PR and we should go ahead with merging this one. |
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change default algorithms
Co-authored-by: Jutho <Jutho@users.noreply.github.com>
Co-authored-by: Jutho <Jutho@users.noreply.github.com>
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Follow-up on #178 to improve on #176.
This consists of quite a few more opinionated changes that I think are warranted but might not be completely fair.
I have renamed a bunch of the LAPACK-like functions across YALAPACK/YAROCSOLVER/YACUSOLVER in an attempt to make life slightly easier, and simply merge all of the implementations into a single big
@evalloop.In the meantime, I also changed the GLA implementations to actually copy in-place, as this is something that has come up quite a few times already and I think overall this is probably more convenient anyways.
The jacobi-svd algorithm that only worked for LAPACK with
m >= nis now also implemented form < nby making use of the samesvd_via_adjointpattern that was already present in some of the GPU solvers, which is now also shared.I think overall this manages to delete quite a bit of code, but the eval loops are also somewhat hard to follow so I'm not sure if this is all that much "simpler" implementation-wise, but at least it does allow selecting
DivideAndConquerin a driver-agnostic way.