用大模型预测技术优化数据库查询调度，性能最高提升6倍。
P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction
- 基于下一个词预测思想，用决策变压器学习硬件调度策略。
- 在B+树索引上实现最高6倍的查询吞吐量提升。
- 适合需要极致性能的数据库系统和异构硬件环境。
自2000年代初德纳德缩放失效后，芯片厂商通过增加核心数而非提升频率来提升性能，带来了NUMA和芯粒架构的兴起。如今，硬件异构性加剧，导致数据库管理系统（DBMS）性能差异可达一个数量级。本文提出P-MOSS，一种基于大语言模型核心思想的智能调度框架，利用硬件性能监控单元采集的低层统计信息，通过决策变换器实现查询执行与数据存储的跨节点协同调度。该框架采用下一个词预测、生成式预训练与微调等机制，具备跨硬件与工作负载的适应能力。在B+树索引场景下的实验表明，相比传统调度方式，P-MOSS可实现高达6倍的查询吞吐量提升。
原文摘要 · Abstract (English)
Ever since the Dennard scaling broke down in the early 2000s and the frequency of the CPUs stalled, vendors have started to increase the core count in each CPU chip at the expense of introducing heterogeneity, thus ushering the era of NUMA and Chiplet processors. Since then, the heterogeneity in the design space of hardware has only increased to the point that DBMS performance may vary significantly up to an order of magnitude in modern servers. An important factor that affects performance includes the location of the logical cores where the DBMS queries execute, and the location where the data resides. This paper introduces P-MOSS, a learned spatial scheduling framework that schedules query execution to specific logical cores, and co-locates data on the corresponding NUMA node. For cross-hardware and workload adaptability, P-MOSS leverages core principles from Large Language Models, such as Next Token prediction, Generative Pre-training, and Fine-tuning. In the spirit of hardware-software synergy, P-MOSS guides its scheduling decision solely based on the low-level hardware statistics collected from the hardware Performance Monitoring Unit with the aid of a Decision Transformer. Experimental evaluation is performed in the context of the B$^+$-Tree index. Performance results demonstrate that P-MOSS offers an improvement of up to $6\times$ over traditional schedules in terms of query throughput.
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