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      "statement": "# Dao and colleagues: IO-aware exact attention with FlashAttention\n\nComputed exact attention with tiled GPU operations and recomputation, reducing transfers between high-bandwidth memory and on-chip storage without materializing the full attention matrix.\n\n## Scientific scope\n\nThe dense algorithm changes memory access, not quadratic arithmetic complexity. IO bounds have an explicit memory model; speedups are hardware-specific. Sparse extensions are approximate and remain distinct.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nTri Dao; Daniel Y. Fu; Stefano Ermon; Atri Rudra; Christopher Ré\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://arxiv.org/abs/2205.14135v2)\n- [Stanford HAI: FlashAttention research record](https://hai.stanford.edu/research/flashattention-fast-and-memory-efficient-exact-attention-with-io-awareness)\n",
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