Summary of Efficient Llm Scheduling by Learning to Rank, By Yichao Fu et al.
Efficient LLM Scheduling by Learning to Rank
by Yichao Fu, Siqi Zhu, Runlong Su, Aurick Qiao, Ion Stoica, Hao Zhang
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a novel scheduler for Large Language Model (LLM) inference and serving, which leverages learning to rank techniques to predict the relative ranks of output lengths in a batch of requests. This approach allows for better scheduling decisions, approximating the shortest-job-first (SJF) schedule more effectively than existing methods. The proposed scheduler is integrated with state-of-the-art LLM serving systems and demonstrates significant performance improvements in applications such as chatbot serving (2.8x lower latency) and synthetic data generation (6.5x higher throughput). The authors also make their code available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves the way computers schedule tasks when using large language models to generate text. Currently, these systems use a simple “first come, first served” approach that can be slow and inefficient. The researchers developed a new scheduling system that uses machine learning to predict how long it will take each task to complete. This allows for more efficient scheduling decisions, resulting in faster responses in applications like chatbots (28% faster) and improved ability to generate large amounts of synthetic data (650% increase). |
Keywords
» Artificial intelligence » Inference » Large language model » Machine learning » Synthetic data
