kernel_tuner
halutmatmul
kernel_tuner | halutmatmul | |
---|---|---|
4 | 3 | |
243 | 201 | |
9.9% | - | |
9.1 | 9.4 | |
5 days ago | 5 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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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
halutmatmul
- Show HN: Stella Nera – Maddness Hardware Accelerator
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10x faster matrix and vector operations
This master's thesis sort of does it, but it doesn't have any fine-tuning yet so it completely wrecks the accuracy: https://github.com/joennlae/halutmatmul.
If someone worked on contributing this to Composer [1] I'd be down to help out. I can't justify building it all on my own right now since we're 100% focused on training speedup, but I could definitely meet and talk through it, help code tricky parts, review PRs, etc.
[1] https://github.com/mosaicml/composer
What are some alternatives?
pyopencl - OpenCL integration for Python, plus shiny features
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
composer - Supercharge Your Model Training
scikit-cuda - Python interface to GPU-powered libraries
bolt - 10x faster matrix and vector operations
BlendLuxCore - Blender Integration for LuxCore
PyTorch-Guide - PyTorch Guide
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.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.