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Sawtooth Wavefront Reordering: Enhanced CuTile FlashAttention on NVIDIA GB10

arXiv 2601.16032 ↗First posted 22 Jan 2026cs.PF cs.AI cs.LGcited by 0

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Abstract (arXiv)

High-performance attention kernels are essential for Large Language Models. This paper presents analysis of CuTile-based Flash Attention memory behavior and a technique to improve its cache performance. In particular, our analysis on the NVIDIA GB10 (Grace Blackwell) identifies the main cause of L2 cache miss. Leveraging this insight, we introduce a new programming technique called Sawtooth Wavefront Reordering that reduces L2 misses. We validate it in both CUDA and CuTile, observing 50% or greater reduction in L2 misses and up to 60% increase in throughput on GB10.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 22 Jan 2026.

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3 of 14 references.

References · 3

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673

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