重新思考GPU时代的分析处理
Rethinking Analytical Processing in the GPU Era
AI总结：
本文提出Sirius，一个基于GPU的SQL引擎，通过即插即用的方式提升数据处理效率，实现显著的成本和速度优势。
摘要：
GPU驱动的数据分析时代已经到来。在本文中，我们主张近年来硬件（如更大GPU内存、更快的互连和IO以及下降的成本）和软件（如可组合的数据系统和成熟的库）的进展已经去除了限制GPU数据分析广泛应用的关键障碍。我们提出了Sirius，一个原型开源的GPU原生SQL引擎，为各种数据系统提供即插即用的加速。Sirius将GPU作为主要引擎，并利用如libcudf等库来实现高性能的关系运算符。它通过利用标准的Substrait查询表示，为现有的数据库提供即插即用的加速，而无需更改面向用户的接口。Sirius在与单节点DuckDB集成时，在TPC-H和ClickBench上分别实现了8.3倍和7.4倍更好的成本效率，在与Apache Doris分布式引擎集成时，可提供高达12.5倍的加速。
英文摘要：
The era of GPU-powered data analytics has arrived. In this paper, we argue that recent advances in hardware (e.g., larger GPU memory, faster interconnect and IO, and declining cost) and software (e.g., composable data systems and mature libraries) have removed the key barriers that have limited the wider adoption of GPU data analytics. We present Sirius, a prototype open-source GPU-native SQL engine that offers drop-in acceleration for diverse data systems. Sirius treats GPU as the primary engine and leverages libraries like libcudf for high-performance relational operators. It provides drop-in acceleration for existing databases by leveraging the standard Substrait query representation, replacing the CPU engine without changing the user-facing interface. Sirius achieves 8.3x and 7.4x better cost efficiency on TPC-H and ClickBench, respectively, when integrated with single-node DuckDB, and delivers up to 12.5x speedup when integrated with Apache Doris distributed engine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
