ThunderKittens: Simple, Fast, and $\textit{Adorable}$ Kernels

Benjamin Frederick Spector, Simran Arora, Aaryan Singhal, Arjun Parthasarathy, Daniel Y Fu, Christopher Re

Keywords: Systems, Kernels, Efficiency, Efficient Models, IO Awareness, GPUs

TL;DR: ThunderKittens (TK) is a framework that simplifies the creation of high-performance AI kernels through key abstractions, enabling efficient implementation of ML architectures on GPU hardware and surpassing previous approaches in hardware utilization.

Abstract: The challenge of mapping AI architectures to GPU hardware is creating a critical bottleneck in AI progress. Despite substantial efforts, hand-written custom kernels fail to meet their theoretical performance thresholds, even on well-established operations like linear attention. The diverse capabilities of GPUs suggests we might we need a wide variety of techniques to achieve high performance. However, our work explores if a small number of key abstractions can drastically simplify the process. We present ThunderKittens (TK), a framework for writing performant AI kernels while remaining easy to use. Our abstractions map to the three levels of the GPU hierarchy: (1) at the warp-level, we provide 16x16 matrix tiles as basic data structures and PyTorch-like operations, (2) at the thread-block level, we provide templates for asynchronously overlapping operations, and (3) at the grid-level, TK helps hide block launch, tear-down, and memory costs. We show the value of TK by providing simple & diverse kernels that match or outperform prior art. We match CuBLAS and FlashAttention-3 on GEMM and attention inference performance and outperform the strongest baselines by $10-40$\% on attention backwards, $8\times$ on state space models, and $14\times$ on linear attention.

Primary Area: infrastructure, software libraries, hardware, systems, etc.

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Submission Number: 9493