What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]

Hi r/MachineLearning,

Stjepan from Manning here, posting with the mods’ permission.

We’ve recently released GPU Programming with Triton by Harshwardhan Fartale in early access. It’s a practical guide to speeding up machine learning training and inference by writing custom GPU kernels in Python with Triton.

The book explains how to identify operations worth optimizing, build and benchmark kernels, fuse operations to reduce memory traffic, implement common parallel and reduction patterns, and improve performance through tiling, vectorization, and better memory access. The goal is to help ML practitioners move beyond framework-level optimization when a model has a stubborn bottleneck.

I’d love to hear from the community:

Which part of your ML workload would you most like to accelerate with a custom kernel—and what currently stops you from writing one?

Real use cases, benchmarks, failed experiments, questions, and skeptical takes are all welcome. We’ll give a free ebook to the five comments that contribute the most to the discussion. The giveaway will remain open for 48 hours, after which we’ll announce the winners here.

Book: https://hubs.la/Q04w2PtF0

50% off with code for the community: MLFARTALE50RE

Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm.

Thank you for having us.

Cheers,

Stjepan