Tuning Flash Attention for Peak NVIDIA cuTile Performance

🔑 Enhanced Key Takeaways

• •CuTile-based Flash Attention fuses softmax and matrix multiplication without materializing the dense attention matrix, streaming Q tiles once and K/V tiles through SRAM for memory savings.[1]
• •Sawtooth Wavefront Reordering alternates K/V tile scan directions to halve L2 cache reuse distance and reduce non-compulsory cache misses by up to 67% on NVIDIA GB10.[1][2]
• •Evaluations on NVIDIA Grace Blackwell GB10 show up to 60% throughput gains for causal and non-causal attention workloads compared to baseline CuTile implementations.[1][3]

🛠️ Technical Deep Dive

• •Adapts split-Q FlashAttention using CuTile's tile-centric abstractions for tensor-core kernels, processing Q-tiles sequentially while streaming K/V tiles from global memory to SRAM.[1]
• •Analysis using hardware counters reveals L1 cache provides negligible benefit for streaming patterns; L2 misses correlate with active SMs due to wavefront data reuse.[2]
• •Raw CUDA implementation with custom CTA scheduling isolates memory effects, confirming deterministic L2 behavior improved by Sawtooth reordering in both CUDA and CuTile.[2]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline