Show HN: Agentic CUDA Kernel Optimizer

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Show HN: Agentic CUDA Kernel Optimizer

by bertaye

Hello; I was working on optimizing some CUDA kernels and I thought may be it is a good oppurtunity learn langgraph as well. I created a simple C++ CUDA Test Harness and handed that to AI agents. They can run kernels, get benchmarks, and even can profile via nsight

fooblaster
219h
Can someone explain why this isn't better accomplished through a single prompt to Claude code or codex? I don't think I understand.
1. bertaye • OP
  017h
  Hello, indeed you can just use that.
  The basic idea here is just automating and limiting the steps that AI can take. These are described as ‘nodes’ and their actions are limited/more descriptive from developer perspective.
  The langgraph simply allows you to set some fences around the AI agent for a goal, instead of raw terminal flow. Is it better? Arguable.
2. osti
  018h
  Yup, this thing is basically useless. I just did /goal optimize the cuda kernel, and ai agent just proposed and tested a bunch of ideas by itself, and it profiled them using nsight ncu etc. by itself, which lead to one order of magnitude faster kernel.
generalizations
120h
Very cool. Did you also try using the karpathy autoresearch? How do you think this compares?
1. bertaye • OP
  020h
  honestly I know it exists but I never used it so can't compare
aidiveyt
016h
in mine, only an agent's final report reaches the orchestrator, never its transcript, so a wrong turn inside a node stays invisible. that's the fence i'd want first.
lohr13
020h
[dead]
asamadx
422h
respect for shipping something this technical solo, this is the kind of project that usually needs a team to even validate correctness. how are you handling regression testing across kernel variants, feels like the hardest part of an agentic optimizer isn't finding a faster kernel, it's proving the faster one didn't quietly break something
1. bertaye • OP
  320h
  That is the funny part actually; we can either provide a reference kernel + input cases for correctness check. In this case at first it will use test harness to run reference kernel with reference inputs ad save the outputs as ground truth. Or we can let AI to create a very basic reference implementation and input cases :D for my own experiments I used second one.