kernel_tuner
kindle_clippings_webapp
| kernel_tuner | kindle_clippings_webapp | |
|---|---|---|
| 4 | 5 | |
| 243 | 9 | |
| 9.9% | - | |
| 9.1 | 7.2 | |
| 5 days ago | about 1 month ago | |
| Python | JavaScript | |
| 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.
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
kindle_clippings_webapp
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Simple Lasts Longer
This is the approach I use in most of my hobby projects. It's simpler, and faster and there are no loading screens.
In my kindle-clippings-manager (https://github.com/karlosos/kindle_clippings_webapp) I import highlights from Kindle and store them in localStorage. The major drawback is a size limitation (10MB). This should not be a problem in most cases but if you need to store more data then indexedDB (Web Storage) can solve the issue.
Linear (https://linear.app/) uses its sync engine to store the data in Web Storage. With optimistic updates, it feels like an offline app. You can read more about the sync engine here: https://news.ycombinator.com/item?id=36519448
- Ask HN: What apps have you created for your own use?
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My react app for managing kindle clippings
Live demo • Source code
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I made an app to manage my kindle clippings (demo data available)
Source code: https://github.com/karlosos/kindle_clippings_webapp
What are some alternatives?
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tf-quant-finance - High-performance TensorFlow library for quantitative finance.
gnar - frp-like Tool with AutoHTTPs Subdomain Proxy
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
notifeed - Watch RSS/Atom feeds and send push notifications/webhooks when new content is detected
scikit-cuda - Python interface to GPU-powered libraries
soundfingerprinting - Open source audio fingerprinting in .NET. An efficient algorithm for acoustic fingerprinting written purely in C#.
BlendLuxCore - Blender Integration for LuxCore
toybox - Opinionated TALL stack starter kit for Laravel solopreneurs