kernel_tuner VS klog

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)

I came up with a file format for time-tracking, which lets me store the data in plain-text files in a human-friendly notation. I also built a corresponding CLI tool for evaluating the files on the terminal.

I’ve been using it almost daily for the past couple of years, and so far it has served me quite well.

Project site / docs: https://klog.jotaen.net

File spec: https://github.com/jotaen/klog/blob/main/Specification.md