GPU-friendly truncation implementations#349
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I think that implementation is very clean. Not sure I can explain all the errors. Some seem to originate from the eigenvectors of a DiagonalTensorMap also being diagonal, which is being changed, right? But other errors I cannot directly explain without running the code locally. |
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I will have a look later, I have a local branch to fix the diagonal implementations already, we'll see what remains after. |
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I think this one still needs a little work to get it over the finish line, but then we should be able to really try CUDA with the TNR and PEPS stuff |
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Your PR no longer requires formatting changes. Thank you for your contribution! |
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wait please don't I'm working on this 😆 |
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I'll stop I just wanted to fix formatting and rebase!!! |
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Hahah but the rebase screws up the git history, now I gotta see how much git magic I know to fix my local branch |
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Oh no I'm sorry! |
This reverts commit 77f0ffa.
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| function MatrixAlgebraKit.findtruncated( | ||
| values::CuSectorVector, strategy::MatrixAlgebraKit.TruncationByError | ||
| ) |
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This probably needs to be special cased for p == Inf, if we want to support this. That in itself should be quite an easy case.
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I just checked, and at least in MatrixAlgebraKit we are currently not supporting this. While I don't necessarily have any strong opinions in favor or against this, I will for now simply add a check and leave this as TBA?
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@kshyatt Do you have any clue why the CUDA tests could be giving segfaults? Is this something I caused or are we running into a different issue? Otherwise I think this PR is ready to get merged, would be great to get this done. |
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CUDA 12 sometimes freaks out due to bad LLVM, I retried the build |
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This is an attempt to get rid of the scalar-indexing oriented approach, and instead do more global operations.
The main point is to take all the singular/eigen values at the same time, and sort them in their entirety.