{"title":"将查询处理与并行语言集成","authors":"Brandon Myers","doi":"10.1109/ICDEW.2015.7129583","DOIUrl":null,"url":null,"abstract":"In this thesis we propose new techniques for using parallel languages to improve query processing. Optimizing a query plan and its particular implementation is important for efficient processing on modern systems. First, we present our work on a parallel representation of queries using partitioned global address space languages that enables new optimizations. Next, we propose future work on cooperative optimization of query plans and imperative programs in the context of parallel applications that include queries.","PeriodicalId":333151,"journal":{"name":"2015 31st IEEE International Conference on Data Engineering Workshops","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Integrating query processing with parallel languages\",\"authors\":\"Brandon Myers\",\"doi\":\"10.1109/ICDEW.2015.7129583\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this thesis we propose new techniques for using parallel languages to improve query processing. Optimizing a query plan and its particular implementation is important for efficient processing on modern systems. First, we present our work on a parallel representation of queries using partitioned global address space languages that enables new optimizations. Next, we propose future work on cooperative optimization of query plans and imperative programs in the context of parallel applications that include queries.\",\"PeriodicalId\":333151,\"journal\":{\"name\":\"2015 31st IEEE International Conference on Data Engineering Workshops\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 31st IEEE International Conference on Data Engineering Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDEW.2015.7129583\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 31st IEEE International Conference on Data Engineering Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2015.7129583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Integrating query processing with parallel languages
In this thesis we propose new techniques for using parallel languages to improve query processing. Optimizing a query plan and its particular implementation is important for efficient processing on modern systems. First, we present our work on a parallel representation of queries using partitioned global address space languages that enables new optimizations. Next, we propose future work on cooperative optimization of query plans and imperative programs in the context of parallel applications that include queries.
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