What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]
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Manning has released "GPU Programming with Triton" by Harshwardhan Fartale, a guide to optimizing machine learning with custom GPU kernels in Python. The book covers identifying optimization opportunities, building and benchmarking kernels, fusing operations, implementing parallel and reduction patterns, and improving performance via tiling, vectorization, and memory access. Manning is seeking community input on ML workload acceleration challenges and is offering a giveaway of five free ebooks for the most insightful contributions to the discussion, which will remain open for 48 hours. A 50% discount code, MLFARTALE50RE, is also available.
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.
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.
Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm.