kernel_tuner
pyopencl
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| kernel_tuner | pyopencl | |
|---|---|---|
| 4 | 2 | |
| 243 | 1,029 | |
| 9.9% | - | |
| 9.1 | 8.1 | |
| 4 days ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kernel_tuner
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Ask HN: What apps have you created for your own use?
I've created Kernel Tuner (https://github.com/KernelTuner/kernel_tuner) as a small software development tool, because I was writing a lot of CUDA and OpenCL kernels at the time. I didn't want to manually figure out what best thread block dimensions and work division among threads were on every GPU over and over again.
The tool evolved quite a bit since the first versions. I'm also using it for testing GPU code, teaching, and it has become one of the main drivers behind a lot of the research that I do.
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PhD'ers, what are you working on? What CS topics excite you?
We have an open science policy, so anyone can use our framework yourself to optimize stuff, if you want! The original paper is linked at the bottom of the GitHub page.
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How to Optimize a CUDA Matmul Kernel for CuBLAS-Like Performance: A Worklog
This is a great post for people who are new to optimizing GPU code.
It is interesting to see that the author got this far without interchanging the innermost loop over k to the outermost loop, as is done in CUTLASS (https://github.com/NVIDIA/cutlass).
As you can see in this blog post the code ends up with a lot of compile-time constants (e.g. BLOCKSIZE, BM, BN, BK, TM, TN) one way to optimize this code further is to use an auto-tuner to find the optimal value for all of these parameters for your GPU and problem size, for example Kernel Tuner (https://github.com/KernelTuner/kernel_tuner)
- Kernel Tuner
pyopencl
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An example for OpenCL 3.0?
Please note that OpenCL consists of two parts: host API and a separate language which is used to write kernels (code which is going to be offloaded to devices). OpenCL specification describes host APIs as C-style APIs and that is what implementors has to provide. However, there are number of various libraries which provides bindings for other languages: - C++ - Python - Go - Rust
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Doubts on pyopencl
I thought the project could be dead, but then I looked into the latest commits to the repository, and it is certainly not dead as a project.
What are some alternatives?
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
PyCUDA - CUDA integration for Python, plus shiny features
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
python-performance - Repository for the book Fast Python - published by Manning
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
scikit-cuda - Python interface to GPU-powered libraries
inventory-hunter - ⚡️ Get notified as soon as your next CPU, GPU, or game console is in stock
BlendLuxCore - Blender Integration for LuxCore
plotoptix - Data visualisation and ray tracing in Python based on OptiX 7.7 framework.
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
LSQR-CUDA - This is a LSQR-CUDA implementation written by Lawrence Ayers under the supervision of Stefan Guthe of the GRIS institute at the Technische Universität Darmstadt. The LSQR library was authored Chris Paige and Michael Saunders.