tmux_super_fingers
kernel_tuner
tmux_super_fingers | kernel_tuner | |
---|---|---|
9 | 4 | |
73 | 246 | |
- | 4.9% | |
4.6 | 9.1 | |
about 1 month ago | 3 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
tmux_super_fingers
-
Ask HN: What apps have you created for your own use?
Years ago I switched from Firefox to Chrome and I was badly missing "translate on mouse hover" from Google Toolbar plug-in. Ended up writing it: https://github.com/artemave/translate_onhover/
I also spent years searching for a way to open file links in vim (all within a tmux session). Ended up writing it: https://github.com/artemave/tmux_super_fingers
-
How do I make these links open in Neovim?
Yeah, i had similar idea in my mind and i forked https://github.com/artemave/tmux_super_fingers and it works like a charm. It's looking chunks of text (e.g. file paths) are highlighted and assigned a character "mark". When user hits the mark key, the highlighted text gets copied to clipboard or open in seperate TMUX pane in $EDITOR in my case it's neovim. Or create new pane with $EDITOR. Very handy.
- tmux_super_fingers: tmux plugin to open file links from the terminal in vim
- tmux_super_fingers: a plugin to open file links from the terminal in vim
- This tmux plugin lets you open file links in vim
- Tmux Super Fingers: open file links in vim, urls in the browser and so on.
- Show HN: Tmux Super Fingers
- Tmux Super Fingers: a tmux plugin to open file links in vim, urls in the browser.
kernel_tuner
-
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.
-
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.
-
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
What are some alternatives?
tmux-open - Tmux key bindings for quick opening of a highlighted file or url
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
tmux-window-name - A plugin to name your tmux windows smartly.
pyopencl - OpenCL integration for Python, plus shiny features
tmuxp - 🖥️ Session manager for tmux, build on libtmux.
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
isomorphic-copy - Cross platform clipboard | networkless! remote copy
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
vim-test - Run your tests at the speed of thought
scikit-cuda - Python interface to GPU-powered libraries
BlendLuxCore - Blender Integration for LuxCore
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.