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针对给定问题，一名助手找到了10篇论文；请你对助手的找到的论文进行打分；请在0/1/2/3分之间打分；打分规则如下：
1. 助手回答的相关性：助手找到的论文的标题和摘要是否针对问题具有相关性：如果非常相关一致，得3分；大部分相关，得2分；小部分相关，得1分；完全不相关，得0分。请注意本规则只考虑相关性，不考虑完整性。
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Flash Attention
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以下是助手找到的论文:
第1篇论文的title: FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision. 对应的abstract:  Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. FlashAttention elaborated an approach to speed up attention on GPUs through minimizing memory reads/writes. However, it has yet to take advantage of new capabilities present in recent hardware, with FlashAttention-2 achieving only 35% utilization on the H100 GPU. We develop three main techniques to speed up attention on Hopper GPUs: exploiting asynchrony of the Tensor Cores and TMA to (1) overlap overall computation and data movement via warp-specialization and (2) interleave block-wise matmul and softmax operations, and (3) block quantization and incoherent processing that leverages hardware support for FP8 low-precision. We demonstrate that our method, FlashAttention-3, achieves speedup on H100 GPUs by 1.5-2.0$\times$ with FP16 reaching up to 740 TFLOPs/s (75% utilization), and with FP8 reaching close to 1.2 PFLOPs/s. We validate that FP8 FlashAttention-3 achieves 2.6$\times$ lower numerical error than a baseline FP8 attention. 
第2篇论文的title: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. 对应的abstract:  Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware -- accounting for reads and writes between levels of GPU memory. We propose FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of FlashAttention, showing that it requires fewer HBM accesses than standard attention, and is optimal for a range of SRAM sizes. We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method. FlashAttention trains Transformers faster than existing baselines: 15% end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the MLPerf 1.1 training speed record, 3$\times$ speedup on GPT-2 (seq. length 1K), and 2.4$\times$ speedup on long-range arena (seq. length 1K-4K). FlashAttention and block-sparse FlashAttention enable longer context in Transformers, yielding higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on long-document classification) and entirely new capabilities: the first Transformers to achieve better-than-chance performance on the Path-X challenge (seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1% accuracy). 
第3篇论文的title: FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning. 对应的abstract:  Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation. The attention layer is the main bottleneck in scaling to longer sequences, as its runtime and memory increase quadratically in the sequence length. FlashAttention exploits the asymmetric GPU memory hierarchy to bring significant memory saving (linear instead of quadratic) and runtime speedup (2-4$\times$ compared to optimized baselines), with no approximation. However, FlashAttention is still not nearly as fast as optimized matrix-multiply (GEMM) operations, reaching only 25-40\% of the theoretical maximum FLOPs/s. We observe that the inefficiency is due to suboptimal work partitioning between different thread blocks and warps on the GPU, causing either low-occupancy or unnecessary shared memory reads/writes. We propose FlashAttention-2, with better work partitioning to address these issues. In particular, we (1) tweak the algorithm to reduce the number of non-matmul FLOPs (2) parallelize the attention computation, even for a single head, across different thread blocks to increase occupancy, and (3) within each thread block, distribute the work between warps to reduce communication through shared memory. These yield around 2$\times$ speedup compared to FlashAttention, reaching 50-73\% of the theoretical maximum FLOPs/s on A100 and getting close to the efficiency of GEMM operations. We empirically validate that when used end-to-end to train GPT-style models, FlashAttention-2 reaches training speed of up to 225 TFLOPs/s per A100 GPU (72\% model FLOPs utilization). 
第4篇论文的title: Is Flash Attention Stable?. 对应的abstract:  Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations training state-of-the-art Generative AI models have reported cases of instability during training, often taking the form of loss spikes. Numeric deviation has emerged as a potential cause of this training instability, although quantifying this is especially challenging given the costly nature of training runs. In this work, we develop a principled approach to understanding the effects of numeric deviation, and construct proxies to put observations into context when downstream effects are difficult to quantify. As a case study, we apply this framework to analyze the widely-adopted Flash Attention optimization. We find that Flash Attention sees roughly an order of magnitude more numeric deviation as compared to Baseline Attention at BF16 when measured during an isolated forward pass. We then use a data-driven analysis based on the Wasserstein Distance to provide upper bounds on how this numeric deviation impacts model weights during training, finding that the numerical deviation present in Flash Attention is 2-5 times less significant than low-precision training. 
第5篇论文的title: A Flash Attention Transformer for Multi-Behaviour Recommendation. 对应的abstract: \beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.
第6篇论文的title: From Online Softmax to FlashAttention. 对应的abstract: ,
第7篇论文的title: Points for Attention in the Creation of Flash Based on the Characteristics of Mobile Phone Hardware. 对应的abstract: With the popularization of mobile data business, the rapid development of the network and improvement of mobile phone performance, the development of the mobile Flash is now well under way. Compared with traditional flsash, mobile flash production have many differences. Now, the influence of the mobile phone hardware on the flash lite creation, as well as the points for attention in the production of flash lite are described.
第8篇论文的title: What Flips Attention?. 对应的abstract: A central feature of our waking mental experience is that our attention naturally toggles back and forth between "external" and "internal" stimuli. In the midst of an externally demanding task, attention can involuntarily shift internally with no clear reason how or why thoughts momentarily shifted inward. In the case of external attention, we are typically exploring and encoding aspects of our external world, whereas internal attention often involves searching for and retrieving potentially relevant information from our memory networks. Cognitive science has traditionally focused on understanding forms of internal and external attention separately, leaving a mystery about what sparks the seemingly automatic shifts between the two. Specifically, what shifts attentional focus from being outward-directed to being inward-directed? We present a candidate mechanism: Familiarity-detection.
第9篇论文的title: Does Animation Attract Online Users' Attention? The Effects of Flash on Information Search Performance and Perceptions. 对应的abstract: The proliferation of information on the Internet poses a significant challenge on humans' limited attentional resources. To attract online users' attention, various kinds of animation are widely used on websites. Despite the ubiquitous use of animation, there is an inadequate understanding of its effect on attention. Focusing on flash animation, this study examines its effects on online users' performance and perceptions in both task-relevant and task-irrelevant information search contexts by drawing on the visual search literature and two theories from cognitive psychology. In the task-relevant context, flash is applied on the search target; while in the task-irrelevant context, flash is applied on a nontarget item. The results of this study confirm that flash does attract users' attention and facilitates quicker location of the flashed target item in tightly packed screen displays. However, there is no evidence that attracting attention increases recall of the flashed item, as is generally presumed in practice, and may even decrease the overall recall. One explanation is that when users have to use their limited attentional resources on suppressing the distraction of flash, they will have less mental resources to process information. Moreover, the results suggest that processing information about an item depends not only on the attention it attracts per se, but also on the attention that other items on the same screen attract. While flashing an item may not increase the recall of that item, it can reduce the recall of other items (especially the nontarget items) on the screen. Finally, flash has negative effects on users' focused attention and attitude towards using the website. These results have implications for website interface design, online product promotion, online advertising, and multimedia training systems, among others.
第10篇论文的title: Attention au Flash rush. 对应的abstract: La memoire Flash s'insinue partout : dans les telephones portables, les cartes memoire, les cles USB, les baladeurs... La concurrence s'annonce rude, le marche explosif. Pour le plus grand plaisir du consommateur.
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根据首先根据上述规则逐条评分并给出详细的理由，尤其当你没有给3分时，请明确说出你认为没有完全满足的原因。最后，请将每篇论文的得分结果汇总起来，得到一个长为10的列表。




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