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Package Details: cutlass 4.8.0-1
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| Git Clone URL: | https://aur.archlinux.org/cutlass.git (read-only, click to copy) |
|---|---|
| Package Base: | cutlass |
| Description: | CUDA Templates for Linear Algebra Subroutines |
| Upstream URL: | https://github.com/NVIDIA/cutlass |
| Keywords: | ai gemm nvidia |
| Licenses: | BSD-3-Clause |
| Submitter: | daskol |
| Maintainer: | Smoolak |
| Last Packager: | Smoolak |
| Votes: | 0 |
| Popularity: | 0.000000 |
| First Submitted: | 2023-06-20 22:59 (UTC) |
| Last Updated: | 2026-09-24 20:17 (UTC) |
Dependencies (19)
- cuda (cuda11.1AUR, cuda-12.2AUR, cuda12.0AUR, cuda11.4AUR, cuda-12.5AUR, cuda-12.8AUR, cuda-pascalAUR, cuda-12.9AUR, cuda-12.6AUR)
- cudnn (cudnn9.10-cuda12.9AUR, cudnn-pascalAUR)
- cmake (cmake3AUR, cmake-gitAUR) (make)
- git (git-gitAUR, git-glAUR, git-wd40AUR) (make)
- ninja (ninja-gitAUR, ninja-memAUR, ninja-noemacs-gitAUR, ninja-kitwareAUR, ninja-fuchsia-gitAUR, n2-gitAUR) (make)
- patchelf (patchelf-gitAUR) (make)
- pybind11 (pybind11-gitAUR) (make)
- python-build (make)
- python-cuda (make)
- python-installer (make)
- python-ml-dtypes (make)
- python-networkx (python-networkx-gitAUR) (make)
- python-numpy (python-numpy-gitAUR, python-numpy-mkl-binAUR, python-numpy1AUR, python-numpy-mkl-tbbAUR, python-numpy-mklAUR) (make)
- python-pydot (make)
- python-scikit-build (make)
- python-setuptools (make)
- python-treelib (make)
- python-wheel (make)
- python-pytest (check)
Required by (2)
- python-cutlass
- sglang-git (optional)
Sources (14)
- complete-cutlass-cppgen-packages.patch
- cuda-python-13.patch
- cuda13-family-arch-aliases.patch
- cutlass-4.8.0.tar.gz
- per-target-architecture-bounds.patch
- sm100-cross-fp8-link-split.patch
- sm100f-sparse-mxf4-target.patch
- sm107-fp4-breuse-epilogue.patch
- sm120-epilogue-tile-filter.patch
- sm120-fp6-small-n-copy-atom.patch
- sm120f-sparse-mxf4-target.patch
- system-cuda-rpath.patch
- system-cutlass-path.patch
- use-ml-dtypes-for-bfloat16.patch
Latest Comments
arzeth commented on 2026-07-04 16:18 (UTC)
I have a suspicion that
-DCUTLASS_NVCC_ARCHS='75;80;86;89;90;90a'should be updated — add120there.Interestingly, in Gentoo (https://gitweb.gentoo.org/repo/gentoo.git/tree/dev-libs/cutlass/cutlass-4.2.1.ebuild), this param is set to
all-major(which means75;80;90;100;110;120according to/usr/share/cmake/Modules/Internal/CMakeCUDAArchitecturesAll.cmakeline 14), but that's not very friendly to RTX 3000, 4000, yet friendly to the rare NVIDIA Jetson.I myself would prefer
native(this package takes long to compile), but other CUDA-related packages on AUR don't usenative. BTW, there's alsoallvalue possible (but that's overkill).lahwaacz commented on 2023-11-30 16:51 (UTC)
Now
pybind11is missing inmakedepends... Also please don't add thenvidiagroup - there is already annvidiapackage in the official repositories, which exists in the same "namespace" as package groups.lahwaacz commented on 2023-11-30 08:51 (UTC)
Also
python-setuptoolsis missing inmakedepends.lahwaacz commented on 2023-11-30 08:43 (UTC)
The hardcoded
-j 48flag is not nice. You might addninjatomakedepends,-G Ninjato the configure command and remove-j 48from the build command. Ninja uses all available cores by default.The build directory can be simply
buildrather than$pkgname-$pkgver/build.Also the prefix should not be
$pkgdir/usr, the right way to do this is with-DCMAKE_INSTALL_PREFIX=/usrat configure step andDESTDIR="$pkgdir" cmake --install <build_directory>as the install command.