Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/kerneltuner/kernel_tuner

Kernel Tuner
https://github.com/kerneltuner/kernel_tuner

auto-tuning autotuning c cplusplus cuda cuda-kernels gpu gpu-computing kernel-tuner machine-learning opencl opencl-kernels optimization python software-development testing

Last synced: about 2 months ago
JSON representation

Kernel Tuner

Host: GitHub
URL: https://github.com/kerneltuner/kernel_tuner
Owner: KernelTuner
License: apache-2.0
Created: 2016-03-28T13:32:17.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2024-04-23T09:30:24.000Z (2 months ago)
Last Synced: 2024-04-23T11:14:01.596Z (2 months ago)
Topics: auto-tuning, autotuning, c, cplusplus, cuda, cuda-kernels, gpu, gpu-computing, kernel-tuner, machine-learning, opencl, opencl-kernels, optimization, python, software-development, testing
Language: Python
Homepage: https://kerneltuner.github.io/kernel_tuner/
Size: 17.5 MB
Stars: 242
Watchers: 9
Forks: 43
Open Issues: 14
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Citation: CITATION.cff

Lists

awesome-green-software - Kernel Tuner - tuner capable of [optimizing GPU code for energy efficiency](https://github.com/KernelTuner/kernel_tuner/blob/master/examples/cuda/going_green_performance_model.py). (Dev / Tooling / Code based)

README

        



  



---

[![Build Status](https://github.com/KernelTuner/kernel_tuner/actions/workflows/build-test-python-package.yml/badge.svg)](https://github.com/KernelTuner/kernel_tuner/actions/workflows/build-test-python-package.yml)

[![CodeCov Badge](https://codecov.io/gh/KernelTuner/kernel_tuner/branch/master/graph/badge.svg)](https://codecov.io/gh/KernelTuner/kernel_tuner)

[![PyPi Badge](https://img.shields.io/pypi/v/kernel_tuner.svg?colorB=blue)](https://pypi.python.org/pypi/kernel_tuner/)

[![Zenodo Badge](https://zenodo.org/badge/54894320.svg)](https://zenodo.org/badge/latestdoi/54894320)

[![SonarCloud Badge](https://sonarcloud.io/api/project_badges/measure?project=KernelTuner_kernel_tuner&metric=alert_status)](https://sonarcloud.io/dashboard?id=KernelTuner_kernel_tuner)

[![OpenSSF Badge](https://bestpractices.coreinfrastructure.org/projects/6573/badge)](https://bestpractices.coreinfrastructure.org/projects/6573)

[![FairSoftware Badge](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)

---

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](https://kerneltuner.github.io/kernel_tuner/stable/optimization.html) to speedup tuning

- Energy measurements and optimizations [(power capping, clock frequency tuning)](https://arxiv.org/abs/2211.07260)

- ... and much more! For example, [caching](https://kerneltuner.github.io/kernel_tuner/stable/cache_files.html), [output verification](https://kerneltuner.github.io/kernel_tuner/stable/correctness.html), [tuning host and device code](https://kerneltuner.github.io/kernel_tuner/stable/hostcode.html), [user defined metrics](https://kerneltuner.github.io/kernel_tuner/stable/metrics.html), see [the full documentation](https://kerneltuner.github.io/kernel_tuner/stable/index.html).

## Installation

- First, make sure you have your [CUDA](https://kerneltuner.github.io/kernel_tuner/stable/install.html#cuda-and-pycuda), [OpenCL](https://kerneltuner.github.io/kernel_tuner/stable/install.html#opencl-and-pyopencl), or [HIP](https://kerneltuner.github.io/kernel_tuner/stable/install.html#hip-and-pyhipl) 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](http://kerneltuner.github.io/kernel_tuner/stable/install.html).

## Example

```python

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
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](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&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}

}

```