---
id: 20260828-T0-10
title: "DataKernelBench：测试LLM能否优化GPU数据库查询"
title_en: "DataKernelBench: Benchmarking LLMs for Optimizing GPU Database Queries"
url: https://ai.daily.yangsir.net/daily/20260828-T0-10
issue_date: 2026-08-28
publish_date: 2026-08-27T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2608.25061
---

# DataKernelBench：测试LLM能否优化GPU数据库查询

新基准测试DataKernelBench发布，专门评估大语言模型（LLM）优化GPU数据库查询内核的能力。现有LLM内核基准测试只关注机器学习算子，而忽略数据库场景中不规则、异构且数据移动密集的内核。该基准填补了这一空白，为评估LLM在数据库性能优化上的潜力提供了新标准。

## English Version

**DataKernelBench: Benchmarking LLMs for Optimizing GPU Database Queries**

A new benchmark, DataKernelBench, evaluates LLMs' ability to optimize GPU database query kernels. Existing LLM benchmarks focus on ML operators, ignoring the irregular, heterogeneous, data-movement-heavy kernels in database scenarios. This benchmark fills that gap, providing a new standard for assessing LLM potential in database performance optimization.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2608.25061)

**详情页**：https://ai.daily.yangsir.net/daily/20260828-T0-10

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