Summary of Thunderkittens: Simple, Fast, and Adorable Ai Kernels, by Benjamin F. Spector et al.
ThunderKittens: Simple, Fast, and Adorable AI Kernels
by Benjamin F. Spector, Simran Arora, Aaryan Singhal, Daniel Y. Fu, Christopher Ré
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a framework called ThunderKittens (TK) that simplifies writing performant AI kernels for GPUs. TK maps to the GPU hierarchy’s three levels: warp-level, thread-block level, and grid-level. The framework provides abstractions for 16×16 matrix tiles, parallel compute operations, overlapping asynchronous operations, and support for hiding block launch and tear-down costs. The authors demonstrate the value of TK by providing kernels that match or outperform prior kernels for various AI operations, such as GEMM, attention inference, attention backwards, state space models, and linear attention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a framework called ThunderKittens (TK) to simplify writing AI kernels for GPUs. The framework provides abstractions at different levels of the GPU hierarchy. This makes it easier to write performant AI kernels while keeping them easy to use and maintain. The authors tested their framework with different AI operations and showed that it can match or outperform existing approaches. |
Keywords
» Artificial intelligence » Attention » Inference
