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[Docs] Update RunningOnGPUs.md (#1499)
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* [Docs] Update RunningOnGPUs.md

* [ITensors] Bump to v0.6.13
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@mtfishman
mtfishman authored Jun 14, 2024
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2 changes: 1 addition & 1 deletion Project.toml
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name = "ITensors"
uuid = "9136182c-28ba-11e9-034c-db9fb085ebd5"
authors = ["Matthew Fishman <[email protected]>", "Miles Stoudenmire <[email protected]>"]
version = "0.6.12"
version = "0.6.13"

[deps]
Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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17 changes: 11 additions & 6 deletions docs/src/RunningOnGPUs.md
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Expand Up @@ -62,9 +62,14 @@ The table below summarizes each backend's current capabilities.

| | CUDA | cuTENSOR | ROCm | Metal | oneAPI |
|------------------------------|------|------------|--------|--------|--------|
| Contractions (dense) | ✓ (cuBLAS) | ✓ | ✓ | ✓ | N/A |
| QR (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU | On CPU | N/A |
| SVD (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU | On CPU | N/A |
| Eigendecomposition (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU | On CPU | N/A |
| Double precision (`Float64`) | ✓ (cuSOLVER) | ✓ | ✓ | N/A | N/A |
| Block sparse | In progress | In progress | In progress | In progress | N/A |
| Contractions (dense) | ✓ (cuBLAS) | ✓ | ✓ | ✓ | N/A[^oneapi] |
| QR (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU[^linalg] | On CPU[^linalg] | N/A[^oneapi] |
| SVD (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU[^linalg] | On CPU[^linalg] | N/A[^oneapi] |
| Eigendecomposition (dense) | ✓ (cuSOLVER) | ✓ (cuSOLVER) | On CPU[^linalg] | On CPU[^linalg] | N/A[^oneapi] |
| Double precision (`Float64`) | ✓ | ✓ | ✓ | N/A[^metal] | N/A[^oneapi] |
| Block sparse | ✓[^blocksparse] | ✓[^blocksparse] | ✓[^blocksparse] | ✓[^blocksparse] | N/A[^oneapi] |

[^linalg]: Some GPU vendors have not implemented certain matrix factorizations, or the ones they have implemented are not efficient compared to running on CPU, so as a workaround we perform those operations on CPU by transferring the data back and forth from GPU to CPU. We will add support for running those operations on GPU as they become available. If your algorithm's cost is dominated by those operations you won't see any speedup by trying to run it on those kinds of GPUs.
[^blocksparse]: Support is experimental. Operations may not be fully optimized and could have bugs.
[^oneapi]: We plan to add Intel GPU support through Julia's oneAPI.jl interface but don't have any Intel GPUs to test on right now.
[^metal]: Apple doesn't support double precision floating point operations on their GPUs, see Section 2.1 of the [Metal Shading Language Specification](https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf). Until it does, we can't support double precision operations on Apple GPUs.

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Registration pull request created: JuliaRegistries/General/109039

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.6.13 -m "<description of version>" 338feb8c2c3a5535370e8ac0b3956c66d6ba6d5a
git push origin v0.6.13

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