The paper "FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision" introduces a new method, FlashAttention-3, to enhance the efficiency and accuracy of attention operations on GPUs. The authors address the computational bottlenecks in large language models and long-context applications by leveraging the asynchrony and low-precision capabilities of modern GPUs, specifically the NVIDIA Hopper architecture. Key contributions of FlashAttention-3 include: 1. **Producer-Consumer Asynchrony**: Utilizing warp-specialization to exploit the asynchronous execution of Tensor Cores and Tensor Memory Accelerator (TMA) to overlap data movement and computation. 2. **Hiding Softmax under Asynchronous Block-wise GEMMs**: Overlapping the low-throughput softmax operations with asynchronous GEMMs to improve overall efficiency. 3. **Hardware-accelerated Low-precision GEMM**: Adapting the forward pass algorithm to target FP8 Tensor Cores for GEMM, achieving nearly double the throughput compared to FP16. The authors demonstrate that FlashAttention-3 achieves a 1.5-2.0x speedup over FlashAttention-2 on H100 GPUs, with FP16 reaching up to 740 TFLOPs/s (75% utilization) and FP8 reaching close to 1.2 PFLOPs/s. They also validate that FP8 FlashAttention-3 reduces numerical error by 2.6x compared to standard per-tensor quantization. The paper includes empirical validation through benchmarking and ablation studies, showing that the proposed techniques significantly improve the performance and accuracy of attention operations. The authors discuss limitations and future directions, emphasizing the potential for further optimization in large-scale training and inference.The paper "FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision" introduces a new method, FlashAttention-3, to enhance the efficiency and accuracy of attention operations on GPUs. The authors address the computational bottlenecks in large language models and long-context applications by leveraging the asynchrony and low-precision capabilities of modern GPUs, specifically the NVIDIA Hopper architecture. Key contributions of FlashAttention-3 include: 1. **Producer-Consumer Asynchrony**: Utilizing warp-specialization to exploit the asynchronous execution of Tensor Cores and Tensor Memory Accelerator (TMA) to overlap data movement and computation. 2. **Hiding Softmax under Asynchronous Block-wise GEMMs**: Overlapping the low-throughput softmax operations with asynchronous GEMMs to improve overall efficiency. 3. **Hardware-accelerated Low-precision GEMM**: Adapting the forward pass algorithm to target FP8 Tensor Cores for GEMM, achieving nearly double the throughput compared to FP16. The authors demonstrate that FlashAttention-3 achieves a 1.5-2.0x speedup over FlashAttention-2 on H100 GPUs, with FP16 reaching up to 740 TFLOPs/s (75% utilization) and FP8 reaching close to 1.2 PFLOPs/s. They also validate that FP8 FlashAttention-3 reduces numerical error by 2.6x compared to standard per-tensor quantization. The paper includes empirical validation through benchmarking and ablation studies, showing that the proposed techniques significantly improve the performance and accuracy of attention operations. The authors discuss limitations and future directions, emphasizing the potential for further optimization in large-scale training and inference.