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Build Status CodeCov Badge PyPi Badge Zenodo Badge SonarCloud Badge OpenSSF Badge FairSoftware Badge

Create optimized GPU applications in any mainstream GPU programming language (CUDA, HIP, OpenCL, OpenACC).

What Kernel Tuner does:

Installation

  • First, make sure you have your CUDA, OpenCL, or HIP compiler installed
  • Then type: pip install kernel_tuner[cuda], pip install kernel_tuner[opencl], or pip install kernel_tuner[hip]
  • or why not all of them: pip install kernel_tuner[cuda,opencl,hip]

More information on installation, also for other languages, in the installation guide.

Example

import numpy as np
from kernel_tuner import tune_kernel

kernel_string = """
__global__ void vector_add(float *c, float *a, float *b, int n) {
    int i = blockIdx.x * block_size_x + threadIdx.x;
    if (i<n) {
        c[i] = a[i] + b[i];
    }
}
"""

n = np.int32(10000000)

a = np.random.randn(n).astype(np.float32)
b = np.random.randn(n).astype(np.float32)
c = np.zeros_like(a)

args = [c, a, b, n]

tune_params = {"block_size_x": [32, 64, 128, 256, 512]}

tune_kernel("vector_add", kernel_string, n, args, tune_params)

More examples here.

Resources

Kernel Tuner ecosystem


C++ magic to integrate auto-tuned kernels into C++ applications


C++ data types for mixed-precision CUDA kernel programming


Monitor, analyze, and visualize auto-tuning runs

Communication & Contribution

  • GitHub Issues: Bug reports, install issues, feature requests, work in progress
  • GitHub Discussion group: General questions, Q&A, thoughts

Contributions are welcome! For feature requests, bug reports, or usage problems, please feel free to create an issue. For more extensive contributions, check the contribution guide.

Citation

If you use Kernel Tuner in research or research software, please cite the most relevant among the publications on Kernel Tuner. To refer to the project as a whole, please cite:

@article{kerneltuner,
  author  = {Ben van Werkhoven},
  title   = {Kernel Tuner: A search-optimizing GPU code auto-tuner},
  journal = {Future Generation Computer Systems},
  year = {2019},
  volume  = {90},
  pages = {347-358},
  url = {https://www.sciencedirect.com/science/article/pii/S0167739X18313359},
  doi = {https://doi.org/10.1016/j.future.2018.08.004}
}