Summary of Optimizing Llm Queries in Relational Workloads, by Shu Liu et al.
Optimizing LLM Queries in Relational Workloads
by Shu Liu, Asim Biswal, Audrey Cheng, Xiangxi Mo, Shiyi Cao, Joseph E. Gonzalez, Ion Stoica, Matei Zaharia
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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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 explores ways to optimize the use of Large Language Models (LLMs) in analytical workloads that invoke these models through native user-defined functions (UDFs). This is particularly useful for tasks like classification, entity extraction, and translation, which are expensive computationally and economically. The researchers propose three optimizations: reordering rows to maximize key-value cache reuse, reordering columns within a row, and deduplicating redundant inference requests. They implement these optimizations in Apache Spark and achieve up to 4.4x improvement in end-to-end latency on diverse LLM-based queries. This is the first work addressing this problem directly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make Large Language Models (LLMs) faster when we use them for tasks like analyzing big data. Right now, using LLMs can be very slow and expensive. To fix this, researchers are looking at ways to speed up LLMs when they’re used inside database queries. They found three ways to do this: reordering the data so that it’s easier to access, rearranging the information within each piece of data, and getting rid of duplicate calculations. By doing these things in Apache Spark, they were able to make their system much faster, which is important for big data analysis. |
Keywords
* Artificial intelligence * Classification * Inference * Translation
