{"title":"rNdN: Fast Query Compilation for NVIDIA GPUs","authors":"Alexander Krolik, Clark Verbrugge, Laurie Hendren","doi":"https://dl.acm.org/doi/10.1145/3603503","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50920,"journal":{"name":"ACM Transactions on Architecture and Code Optimization","volume":"7 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Architecture and Code Optimization","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3603503","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
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.
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.