Show HN: Agentic CUDA Kernel Optimizer

bbertaye about 4 hours ago 4 commentsRead Article on github.com

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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

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Discussion (4 Comments)Read Original on HackerNews

generalizations•43 minutes ago

Very cool. Did you also try using the karpathy autoresearch? How do you think this compares?

bertaye•13 minutes ago

honestly I know it exists but I never used it so can't compare

asamadx•about 3 hours ago

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

bertaye•15 minutes ago

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.