{"title":"RDBMS上图BI查询的微架构分析","authors":"Rathijit Sen, Yuanyuan Tian","doi":"10.1145/3592980.3595321","DOIUrl":null,"url":null,"abstract":"We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB overheads, and overall low (but short-term high) memory bandwidth utilization. Using huge pages increased query performance by up to 65% and workload performance by 23%.","PeriodicalId":400127,"journal":{"name":"Proceedings of the 19th International Workshop on Data Management on New Hardware","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Microarchitectural Analysis of Graph BI Queries on RDBMS\",\"authors\":\"Rathijit Sen, Yuanyuan Tian\",\"doi\":\"10.1145/3592980.3595321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB overheads, and overall low (but short-term high) memory bandwidth utilization. Using huge pages increased query performance by up to 65% and workload performance by 23%.\",\"PeriodicalId\":400127,\"journal\":{\"name\":\"Proceedings of the 19th International Workshop on Data Management on New Hardware\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th International Workshop on Data Management on New Hardware\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3592980.3595321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th International Workshop on Data Management on New Hardware","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3592980.3595321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Microarchitectural Analysis of Graph BI Queries on RDBMS
We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB overheads, and overall low (but short-term high) memory bandwidth utilization. Using huge pages increased query performance by up to 65% and workload performance by 23%.