{"title":"r3d3: gpu上的优化查询编译","authors":"Alexander Krolik, Clark Verbrugge, L. Hendren","doi":"10.1109/CGO51591.2021.9370323","DOIUrl":null,"url":null,"abstract":"Query compilation is an effective approach to improve the performance of repeated database queries. GPU-based approaches have significant promise, but face difficulties in managing compilation time, data transfer costs, and in addressing a reasonably comprehensive range of SQL operations. In this work we describe a hybrid AoT/JIT approach to GPU-based query compilation. We use multiple optimizations to reduce execution, compile, and data transfer times, improving performance over both other GPU-based approaches and CPU-based query compilers as well. Our design addresses a wide range of SQL queries, sufficient to demonstrate the practicality of using GPUs for query optimization.","PeriodicalId":275062,"journal":{"name":"2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"r3d3: Optimized Query Compilation on GPUs\",\"authors\":\"Alexander Krolik, Clark Verbrugge, L. Hendren\",\"doi\":\"10.1109/CGO51591.2021.9370323\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Query compilation is an effective approach to improve the performance of repeated database queries. GPU-based approaches have significant promise, but face difficulties in managing compilation time, data transfer costs, and in addressing a reasonably comprehensive range of SQL operations. In this work we describe a hybrid AoT/JIT approach to GPU-based query compilation. We use multiple optimizations to reduce execution, compile, and data transfer times, improving performance over both other GPU-based approaches and CPU-based query compilers as well. Our design addresses a wide range of SQL queries, sufficient to demonstrate the practicality of using GPUs for query optimization.\",\"PeriodicalId\":275062,\"journal\":{\"name\":\"2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CGO51591.2021.9370323\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGO51591.2021.9370323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Query compilation is an effective approach to improve the performance of repeated database queries. GPU-based approaches have significant promise, but face difficulties in managing compilation time, data transfer costs, and in addressing a reasonably comprehensive range of SQL operations. In this work we describe a hybrid AoT/JIT approach to GPU-based query compilation. We use multiple optimizations to reduce execution, compile, and data transfer times, improving performance over both other GPU-based approaches and CPU-based query compilers as well. Our design addresses a wide range of SQL queries, sufficient to demonstrate the practicality of using GPUs for query optimization.