Summary of Enhancing Performance and Scalability Of Large-scale Recommendation Systems with Jagged Flash Attention, by Rengan Xu et al.
Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention
by Rengan Xu, Junjie Yang, Yifan Xu, Hong Li, Xing Liu, Devashish Shankar, Haoci Zhang, Meng Liu, Boyang Li, Yuxi Hu, Mingwei Tang, Zehua Zhang, Tunhou Zhang, Dai Li, Sijia Chen, Gian-Paolo Musumeci, Jiaqi Zhai, Bill Zhu, Hong Yan, Srihari Reddy
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an efficient approach for exploring complex ranking paradigms in modern recommendation systems. The authors address the challenge of GPU-based computational costs by developing Jagged Feature Interaction Kernels (JFIK), a novel method that extracts fine-grained insights from long categorical features through efficient handling of dynamically sized tensors. Additionally, they enhance attention mechanisms by integrating JFIK with Flash Attention, achieving up to 9x speedup and 22x memory reduction compared to dense attention. The novel Jagged Flash Attention outperforms dense flash attention, with up to 3x speedup and 53% more memory efficiency. In production models, the approach leads to a 10% QPS improvement and 18% memory savings, enabling scaling of recommendation systems with longer features and more complex architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it possible for recommendation systems to be more efficient and handle large amounts of data. It does this by creating new ways for computers to understand and use information from long lists or categories. This helps improve how well the system can predict what people might like, which is important for making good recommendations. |
Keywords
* Artificial intelligence * Attention
