This episode looks at 'FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness', a novel attention algorithm that significantly improves the speed and memory efficiency of Transformers, particularly for handling long sequences. The authors argue that existing approximate attention methods fail to achieve optimal wall-clock speedup because they ignore the importance of I/O-awareness, neglecting the time spent on data transfer between different levels of memory. FlashAttention uses tiling to reduce the number of memory reads and writes between GPU high bandwidth memory (HBM) and on-chip SRAM. This results in faster training times for Transformer models such as BERT and GPT-2, as well as improved model quality by enabling the use of longer sequences. The document also presents a block-sparse FlashAttention, a sparse attention algorithm which further accelerates training and scales Transformers to even longer sequences, achieving better-than-chance performance on the Path-X and Path-256 challenges. Benchmarks are presented comparing FlashAttention and block-sparse FlashAttention against standard and approximate attention implementations, demonstrating their superior performance in terms of runtime and memory usage.