Merge branch 'master' of https://github.com/benvanwerkhoven/kernel_tuner

Author: fjwillemsen

README.md

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+
+
+<div align="center">
+  <img width="500px" src="doc/images/KernelTuner-logo.png"/>
+</div>
+
+---
+[![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/)
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+[![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<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](https://kerneltuner.github.io/kernel_tuner/stable/examples.html).
+
+## Resources
+
+- [Full documentation](https://kerneltuner.github.io/kernel_tuner/stable/)
+- Guides:
+  - [Getting Started](https://kerneltuner.github.io/kernel_tuner/stable/quickstart.html)
+  - [Convolution](https://kerneltuner.github.io/kernel_tuner/stable/convolution.html)
+  - [Diffusion](https://kerneltuner.github.io/kernel_tuner/stable/diffusion.html)
+  - [Matrix Multiplication](https://kerneltuner.github.io/kernel_tuner/stable/matrix_multiplication.html)
+- Features & Use cases:
+  - [Full list of examples](https://kerneltuner.github.io/kernel_tuner/stable/examples.html)
+  - [Output verification](https://kerneltuner.github.io/kernel_tuner/stable/correctness.html)
+  - [Test GPU code from Python](https://github.com/KernelTuner/kernel_tuner/blob/master/examples/cuda/test_vector_add.py)
+  - [Tune code in both host and device code](https://kerneltuner.github.io/kernel_tuner/stable/hostcode.html)
+  - [Optimization algorithms](https://kerneltuner.github.io/kernel_tuner/stable/optimization.html)
+  - [Mixed-precision & Accuracy tuning](https://github.com/KernelTuner/kernel_tuner/blob/master/examples/cuda/accuracy.py)
+  - [Custom metrics & tuning objectives](https://kerneltuner.github.io/kernel_tuner/stable/metrics.html)
+- **Kernel Tuner Tutorial** slides [[PDF]](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/slides/2022_SURF/SURF22-Kernel-Tuner-Tutorial.pdf), hands-on:
+  - Vector add example [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/00_Kernel_Tuner_Introduction.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/00_Kernel_Tuner_Introduction.ipynb)
+  - Tuning thread block dimensions [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/01_Kernel_Tuner_Getting_Started.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/01_Kernel_Tuner_Getting_Started.ipynb)
+  - Search space restrictions & output verification [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/02_Kernel_Tuner_Intermediate.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/02_Kernel_Tuner_Intermediate.ipynb)
+  - Visualization & search space optimization [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/03_Kernel_Tuner_Advanced.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/hands-on/cuda/03_Kernel_Tuner_Advanced.ipynb)
+- **Energy Efficient GPU Computing** tutorial slides [[PDF]](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/slides/2023_Supercomputing/SC23.pdf), hands-on:
+  - Kernel Tuner for GPU energy measurements [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/energy/00_Kernel_Tuner_Introduction.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/energy/00_Kernel_Tuner_Introduction.ipynb)
+  - Code optimizations for energy [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/energy/01_Code_Optimizations_for_Energy.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/energy/01_Code_Optimizations_for_Energy.ipynb)
+  - Mixed precision and accuracy tuning [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/energy/02_Mixed_precision_programming.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/energy/02_Mixed_precision_programming.ipynb)
+  - Optimzing for time vs for energy [[.ipynb](https://github.com/KernelTuner/kernel_tuner_tutorial/blob/master/energy/03_energy_efficient_computing.ipynb)] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/KernelTuner/kernel_tuner_tutorial/blob/master/energy/03_energy_efficient_computing.ipynb)
+
+
+## Kernel Tuner ecosystem
+
+<img width="250px" src="doc/images/kernel_launcher.png"/><br />C++ magic to integrate auto-tuned kernels into C++ applications 
+
+<img width="250px" src="doc/images/kernel_float.png"/><br />C++ data types for mixed-precision CUDA kernel programming
+
+<img width="275px" src="doc/images/kernel_dashboard.png"/><br />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}
+}
+```
+