cuda-tile 1.3.0

CUDA Tile Compiler

cuTile Python

cuTile Python is a programming language for NVIDIA GPUs. The official documentation can be found on docs.nvidia.com, or built from source located in the docs folder.

Example

# This examples uses CuPy which can be installed via `pip install cupy-cuda13x`
# Make sure cuda toolkit 13.1+ is installed: https://developer.nvidia.com/cuda-downloads

import cuda.tile as ct
import cupy
import numpy as np

TILE_SIZE = 16

# cuTile kernel for adding two dense vectors. It runs in parallel on the GPU.
@ct.kernel
def vector_add_kernel(a, b, result):
    block_id = ct.bid(0)
    a_tile = ct.load(a, index=(block_id,), shape=(TILE_SIZE,))
    b_tile = ct.load(b, index=(block_id,), shape=(TILE_SIZE,))
    result_tile = a_tile + b_tile
    ct.store(result, index=(block_id,), tile=result_tile)

# Generate input arrays
rng = cupy.random.default_rng()
a = rng.random(128)
b = rng.random(128)
expected = cupy.asnumpy(a) + cupy.asnumpy(b)

# Allocate an output array and launch the kernel
result = cupy.zeros_like(a)
grid = (ct.cdiv(a.shape[0], TILE_SIZE), 1, 1)
ct.launch(cupy.cuda.get_current_stream(), grid, vector_add_kernel, (a, b, result))

# Verify the results
result_np = cupy.asnumpy(result)
np.testing.assert_array_almost_equal(result_np, expected)

More examples can be found at Samples and TileGym.

System Requirements

cuTile Python generates kernels based on Tile IR which requires NVIDIA Driver r580 or later to run. Furthermore, the tileiras compiler (version 13.2) only supports Blackwell GPU and Ampere/Ada GPU. Hopper GPU will be supported in the coming versions. Checkout the prerequisites for full list of requirements.

Installing from PyPI

cuTile Python is published on PyPI under the cuda-tile package name and can be installed with pip:

pip install cuda-tile[tileiras]

The optional tileiras dependency installs the tileiras compiler directly into your python environment.

If you do not want to have tileiras inside the python environment, run

pip install cuda-tile

and install CUDA Toolkit 13.1+ separately.

On a Debian-based system, use apt-get install cuda-tileiras-13.2 cuda-compiler-13.2 instead of apt-get install cuda-toolkit-13.2 if you wish to avoid installing the full CUDA Toolkit.

Building from Source

cuTile is written mostly in Python, but includes a C++ extension which needs to be built. You will need:

• A C++17-capable compiler, such as GNU C++ or MSVC;
• CMake 3.18+;
• GNU Make on Linux or msbuild on Windows;
• Python 3.10+ with development headers (venv module is recommended but optional);
• CUDA Toolkit 13.1+

On an Ubuntu system, the first four dependencies can be installed with APT:

sudo apt-get update && sudo apt-get install build-essential cmake python3-dev python3-venv

The CMakeLists.txt script will also automatically download the DLPack dependency from GitHub. If you wish to disable this behavior and provide your own copy of DLPack, set the CUDA_TILE_CMAKE_DLPACK_PATH environment variable to a local path to the DLPack source tree.

Unless you are already using a Python virtual environment, it is recommended to create one in order to avoid installing cuTile globally:

python3 -m venv env
source env/bin/activate

Once the build dependencies are in place, the simplest way to build cuTile is to install it in editable mode by running the following command in the source root directory:

pip install -e .

This will create the build directory and invoke the CMake-based build process. In editable mode, the compiled extension module will be placed in the build directory, and then a symbolic link to it will be created in the source directory. This makes sure that the pip install -e . command above is needed only once, and recompiling the extension after making changes to the C++ code can be done with make -C build which is much faster. This logic is defined in setup.py.

Experimental Features (Optional)

cuTile now provides an experimental package containing APIs that are still under active development. These are not part of the stable cuda.tile API and may change.

To enable the experimental features when working from a source checkout, install the experimental package from the repository root:

pip install ./experimental

You can also install it directly from a GitHub repository subdirectory:

pip install \
  "git+https://github.com/NVIDIA/cutile-python.git#egg=cuda-tile-experimental&subdirectory=experimental"

For example, this will make the experimental namespace available for autotuner:

from cuda.tile_experimental import autotune_launch, clear_autotune_cache

Running Tests

cuTile uses the pytest framework for testing. Tests have extra dependencies, such as PyTorch, which can be installed with

pip install -r test/requirements.txt

The tests are located in the test/ directory. To run a specific test file, for example test_copy.py, use the following command:

pytest test/test_copy.py

Copyright and License Information

Copyright © 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

cuTile-Python is licensed under the Apache 2.0 license. See the LICENSES folder for the full license text.