Title: Terabyte-Scale Analytics in the Blink of an Eye Show Chinese title

Title: 眨眼之间达到TB级的分析

Abstract: For the past two decades, the DB community has devoted substantial research to take advantage of cheap clusters of machines for distributed data analytics -- we believe that we are at the beginning of a paradigm shift. The scaling laws and popularity of AI models lead to the deployment of incredibly powerful GPU clusters in commercial data centers. Compared to CPU-only solutions, these clusters deliver impressive improvements in per-node compute, memory bandwidth, and inter-node interconnect performance. In this paper, we study the problem of scaling analytical SQL queries on distributed clusters of GPUs, with the stated goal of establishing an upper bound on the likely performance gains. To do so, we build a prototype designed to maximize performance by leveraging ML/HPC best practices, such as group communication primitives for cross-device data movements. This allows us to conduct thorough performance experimentation to point our community towards a massive performance opportunity of at least 60$\times$. To make these gains more relatable, before you can blink twice, our system can run all 22 queries of TPC-H at a 1TB scale factor! Show Chinese abstract

Abstract: 在过去的二十年里，数据库（DB）社区投入了大量研究来利用廉价的机器集群进行分布式数据分析——我们认为我们正处于范式转变的开端。AI模型的扩展定律和流行度导致商业数据中心部署了极其强大的GPU集群。与仅使用CPU的解决方案相比，这些集群在每个节点的计算能力、内存带宽以及节点间互联性能方面带来了显著提升。本文研究了在GPU分布式集群上扩展分析型SQL查询的问题，目标是确定可能的性能增益上限。为此，我们构建了一个原型系统，通过采用机器学习/高性能计算（ML/HPC）的最佳实践（如跨设备数据移动的组通信原语）来最大化性能。这使我们能够进行全面的性能实验，指出我们的社区至少存在60$\times$的巨大性能提升机会。为了让这些收益更具可比性，在你眨眼之间，我们的系统就能以1TB的规模因子运行TPC-H的所有22个查询！