Mikochi
kernel_tuner
Mikochi | kernel_tuner | |
---|---|---|
6 | 4 | |
153 | 247 | |
- | 5.3% | |
8.5 | 9.1 | |
30 days ago | 5 days ago | |
JavaScript | 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.
Mikochi
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Ask HN: What apps have you created for your own use?
I've created Mikochi (https://github.com/zer0tonin/Mikochi) for myself. It's a file manager for your personal server / NAS, that also allows you to stream files to VLC/MPV.
Before creating Mikochi, I used to access my collection of movies through Jellyfin. Jellyfin has a really nice UI and does a ton of things like adding metadata, but I didn't use those things. I also didn't use their in-browser video player because it didn't work with H265. In addition to that, I wanted to easily manage the files without having to switch to sftp. Mikochi lets me easily create, delete, rename, download, and upload files (or whole directories).
As a bonus, it only requires 26MB of RAM to run on my server.
- Mikochi: Open-source, web based file browser with streaming capabilities
- Show HN: I created a minimalist file-browser web application
- Mikochi - a minimalist remote file browser with a Preact frontend
- Mikochi - a minimalist remote file browser with a Go backend
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I created a minimalist file browser web UI, with streaming capabilites
Installing it is as simple as doing: wget -c https://github.com/zer0tonin/Mikochi/releases/download/1.2.3/mikochi-linux-amd64.tar.gz -O - | tar -xz HOST=127.0.0.1:8080 USERNAME=zer0tonin PASSWORD=horsebatterysomething ./mikochi
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
What are some alternatives?
Gossa - 🎶 a fast and simple multimedia fileserver
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
bitbar - Put the output from any script or program into your macOS Menu Bar (the BitBar reboot)
pyopencl - OpenCL integration for Python, plus shiny features
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
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.
jiro-nn - A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.
pla-reverse-gui - PySide6-based GUI for Seed Cracking and RNG w/o CFW assistance in Pokemon: Legends Arceus
OpenCL-examples - Simple OpenCL examples for exploiting GPU computing