What kinds of ML bottlenecks are a good fit for Triton? [Manning giveaway] [D]
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Manning出版社推出了Harshwardhan Fartale撰写的《GPU Programming with Triton》一书，该书旨在通过使用Triton在Python中编写自定义GPU内核，加速机器学习的训练和推理过程。书中详细介绍了如何识别值得优化的操作、构建和基准测试内核、融合操作以减少内存流量、实现常见的并行和归约模式，并通过分块、向量化和改进内存访问来提升性能。Manning正在社区征集关于机器学习工作负载加速的经验和挑战，并为最有价值的讨论贡献者提供五本免费电子书，此赠送活动将持续48小时。此外，社区成员还可以使用折扣码MLFARTALE50RE享受五折优惠。
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.