kernel-tuner

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

What Kernel Tuner does:

• Works as an external tool to benchmark and optimize GPU kernels in isolation
• Can be used directly on existing kernel code without extensive changes
• Can be used with applications in any host programming language
• Blazing fast search space construction
• More than 20 optimization algorithms to speedup tuning
• Energy measurements and optimizations (power capping, clock frequency tuning)
• ... and much more! For example, caching, output verification, tuning host and device code, user defined metrics, see the full documentation.

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<br>C++ magic to integrate auto-tuned kernels into C++ applications 

<img src="doc/images/kernel_float.png" width="250px"><br>C++ data types for mixed-precision CUDA kernel programming

<img src="doc/images/kernel_dashboard.png" width="275px"><br>Monitor, analyze, and visualize auto-tuning runs


## Communication &amp; Contribution

- GitHub [Issues](https://github.com/KernelTuner/kernel_tuner/issues): Bug reports, install issues, feature requests, work in progress
- GitHub [Discussion group](https://github.com/orgs/KernelTuner/discussions): General questions, Q&amp;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](http://kerneltuner.github.io/kernel_tuner/stable/contributing.html).

## Citation

If you use Kernel Tuner in research or research software, please cite the most relevant among the [publications on Kernel 
Tuner](https://kerneltuner.github.io/kernel_tuner/stable/#citation). To refer to the project as a whole, please cite:

```latex
@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}
}