Summary of Is the Gpu Half-empty or Half-full? Practical Scheduling Techniques For Llms, by Ferdi Kossmann et al.
Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs
by Ferdi Kossmann, Bruce Fontaine, Daya Khudia, Michael Cafarella, Samuel Madden
First submitted to arxiv on: 23 Oct 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 abstract presents a study on improving the serving systems for Large Language Models (LLMs), specifically focusing on scheduling decisions when processing multiple requests concurrently. The authors highlight the challenges of multiplexing hardware resources between concurrent requests and discuss how practical serving systems typically implement load balancing and engine-level scheduling. They survey existing literature-based schedulers and compare them to those used in practical deployments, finding that while literature-based schedulers achieve good performance, they introduce complexity. In contrast, practical deployment schedulers leave performance gains untapped but are easy to implement and deploy. The authors propose two new scheduling techniques that are simple to implement and outperform current methods on production workload traces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Serving systems for Large Language Models (LLMs) help lots of people use the internet at the same time. This is cool, but it’s hard to make sure everyone gets what they need quickly. Imagine you’re running a store with many cashiers, and each cashier helps a customer. You want to make sure that all customers get helped as soon as possible. But sometimes some customers might have to wait because others are taking too long. The paper looks at how people solve this problem in real-world systems and compares it to what the smart folks have suggested doing. They find that the smart folks’ ideas work well, but they’re not easy to use. So, the authors come up with two new ideas that are simple and work better than what’s currently being used. |
