chars VS kernel_tuner

All of mine are CLI...

https://github.com/jftuga/less-Windows - [not really mine, but I just help maintain the port] - GNU less compiled for Windows 10 & 11. Stand-alone version with no dependencies.

https://github.com/jftuga/gofwd - A cross-platform TCP port forwarder with Duo 2FA and Geo-IP integration

https://github.com/jftuga/spotprice - Quickly get AWS spot instance pricing - a bit easier to use than the aws cli; is also faster and has more features

https://github.com/jftuga/tcpscan - A standalone, fast, simple, multi-threaded cross-platform IPv4 TCP port scanner

https://github.com/jftuga/ipinfo - Return IP address info including geographic location and distance when given IP address, email address, host name or URL

https://github.com/jftuga/photo_id_resizer - Resize photo ID images using face recognition technology

https://github.com/jftuga/chars - Determine the end-of-line format, tabs, bom, and nul characters

! [rejected] main -> main (non-fast-forward) error: failed to push some refs to 'https://github.com/jftuga/chars.git' hint: Updates were rejected because the tip of your current branch is behind hint: its remote counterpart. Integrate the remote changes (e.g. hint: 'git pull ...') before pushing again. hint: See the 'Note about fast-forwards' in 'git push --help' for details. ```

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.

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.

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)