# pybind11-cuda

**Repository Path**: yunfeiliu/pybind11-cuda

## Basic Information

- **Project Name**: pybind11-cuda
- **Description**: https://github.com/pkestene/pybind11-cuda
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-24
- **Last Updated**: 2024-07-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# pybind11-cuda

Starting point for GPU accelerated python libraries

Adapted from original work from https://github.com/PWhiddy/pybind11-cuda

Present work uses [modern CMake/Cuda](https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9444-build-systems-exploring-modern-cmake-cuda-v2.pdf) approach

# Prerequisites

Cuda

Python 3.6 or greater

Cmake >= 3.12 (for CUDA support and the new FindPython3 module)

# Build instructions

## cmake

If you use cmake version >= 3.18, you can use [variable CMAKE_CUDA_ARCHITECTURES](https://cmake.org/cmake/help/latest/variable/CMAKE_CUDA_ARCHITECTURES.html) instead of CUDAFLAGS:


```bash
mkdir build; cd build
# provide a default cuda hardware architecture to build for
cmake -DCMAKE_CUDA_ARCHITECTURES="75" -DPython3_EXECUTABLE=`which python` ..
make
```

Please note that specifiying `Python3_EXECUTABLE` is not required, but recommended if you have multiple python executable on your system (e.g. one from OS, another from conda, etc...); this way you can control which python installation will be used.

If you have an older version cmake, you can pass nvcc flags to cmake using env variable `CUDAFLAGS`

```bash
# change CUDAFLAGS according to your target GPU architecture
mkdir build; cd build
# provide a default cuda hardware architecture to build for
export CUDAFLAGS="-arch=sm_75"
cmake -DPython3_EXECUTABLE=`which python` ..
make
```

## test

Test it with
```shell
cd src
python3 test_mul.py
```

_gpu_library.so_ and _test_mul.py_ must be in the same folder. Alternatively you can path to _gpu_library.so_ to your PYTHONPATH env variable.

# Features demonstrated

- Compiles out of the box with cmake
- Numpy integration
- C++ Templating for composable kernels with generic data types

Originally based on https://github.com/torstem/demo-cuda-pybind11