{"title":"设计100tb的湍流数据库(或者如何在家跟踪粒子)","authors":"E. Perlman, R. Burns","doi":"10.1145/1188455.1188625","DOIUrl":null,"url":null,"abstract":"We describe a new environment for large-scale turbulence simulations that uses a cluster of database nodes to store the complete space-time history of fluid velocities. This allows for rapid access to high resolution data that were traditionally too large to store and too computationally expensive to produce on demand.We perform the actual experimental analysis inside the database nodes, which allows for data-intensive computations to be performed across a large number of nodes with relatively little network traffic.We currently have a limited-scale prototype system running actual turbulence simulations and are in the process of establishing a production cluster with high-resolution data. We will discuss our design choices and initial results with load balancing a data-intensive, migratory workload.","PeriodicalId":115940,"journal":{"name":"Proceedings of the 2006 ACM/IEEE conference on Supercomputing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Engineering the 100 terabyte turbulence database (or how to track particles at home)\",\"authors\":\"E. Perlman, R. Burns\",\"doi\":\"10.1145/1188455.1188625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a new environment for large-scale turbulence simulations that uses a cluster of database nodes to store the complete space-time history of fluid velocities. This allows for rapid access to high resolution data that were traditionally too large to store and too computationally expensive to produce on demand.We perform the actual experimental analysis inside the database nodes, which allows for data-intensive computations to be performed across a large number of nodes with relatively little network traffic.We currently have a limited-scale prototype system running actual turbulence simulations and are in the process of establishing a production cluster with high-resolution data. We will discuss our design choices and initial results with load balancing a data-intensive, migratory workload.\",\"PeriodicalId\":115940,\"journal\":{\"name\":\"Proceedings of the 2006 ACM/IEEE conference on Supercomputing\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM/IEEE conference on Supercomputing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1188455.1188625\",\"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 2006 ACM/IEEE conference on Supercomputing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1188455.1188625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Engineering the 100 terabyte turbulence database (or how to track particles at home)
We describe a new environment for large-scale turbulence simulations that uses a cluster of database nodes to store the complete space-time history of fluid velocities. This allows for rapid access to high resolution data that were traditionally too large to store and too computationally expensive to produce on demand.We perform the actual experimental analysis inside the database nodes, which allows for data-intensive computations to be performed across a large number of nodes with relatively little network traffic.We currently have a limited-scale prototype system running actual turbulence simulations and are in the process of establishing a production cluster with high-resolution data. We will discuss our design choices and initial results with load balancing a data-intensive, migratory workload.