rNdN: Fast Query Compilation for NVIDIA GPUs

IF 1.5 3区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Alexander Krolik, Clark Verbrugge, Laurie Hendren
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引用次数: 0

Abstract

GPU database systems are an effective solution to query optimization, particularly with compilation and data caching. They fall short, however, in end-to-end workloads, as existing compiler toolchains are too expensive for use with short-running queries. In this work, we define and evaluate a runtime-suitable query compilation pipeline for NVIDIA GPUs that extracts high performance with only minimal optimization. In particular, our balanced approach successfully trades minor slowdowns in execution for major speedups in compilation, even as data sizes increase. We demonstrate performance benefits compared to both CPU and GPU database systems using interpreters and compilers, extending query compilation for GPUs beyond cached use cases.

rNdN: NVIDIA gpu快速查询编译
GPU数据库系统是查询优化的有效解决方案,特别是在编译和数据缓存方面。然而,在端到端工作负载中,它们的作用不大,因为现有的编译器工具链对于短时间运行的查询来说太昂贵了。在这项工作中,我们为NVIDIA gpu定义并评估了一个适合运行时的查询编译管道,该管道仅通过最小的优化即可提取高性能。特别是,我们的平衡方法成功地以执行上的小减速换取了编译上的大加速,即使在数据大小增加时也是如此。我们演示了与使用解释器和编译器的CPU和GPU数据库系统相比的性能优势,将GPU的查询编译扩展到缓存用例之外。
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来源期刊
ACM Transactions on Architecture and Code Optimization
ACM Transactions on Architecture and Code Optimization 工程技术-计算机:理论方法
CiteScore
3.60
自引率
6.20%
发文量
78
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Architecture and Code Optimization (TACO) focuses on hardware, software, and system research spanning the fields of computer architecture and code optimization. Articles that appear in TACO will either present new techniques and concepts or report on experiences and experiments with actual systems. Insights useful to architects, hardware or software developers, designers, builders, and users will be emphasized.
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