{"title":"在并行磁盘系统上进行结构化排列","authors":"L. Wisniewski","doi":"10.1145/236017.236047","DOIUrl":null,"url":null,"abstract":"The ability to perform permutations of large data sets in place reduces the amount of necessary available disk storage. The simplest way to perform a permutation often is to read the records of a data set from a source portion of data storage, permute them in memory, and write them to a separate target portion of the same size. It can be quite expensive, however, to provide disk storage that is twice the size of very large data sets. Permuting in place reduces the expense by using only a small amount of extra disk storage beyond the size of the data set.","PeriodicalId":442608,"journal":{"name":"Workshop on I/O in Parallel and Distributed Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Structured permuting in place on parallel disk systems\",\"authors\":\"L. Wisniewski\",\"doi\":\"10.1145/236017.236047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ability to perform permutations of large data sets in place reduces the amount of necessary available disk storage. The simplest way to perform a permutation often is to read the records of a data set from a source portion of data storage, permute them in memory, and write them to a separate target portion of the same size. It can be quite expensive, however, to provide disk storage that is twice the size of very large data sets. Permuting in place reduces the expense by using only a small amount of extra disk storage beyond the size of the data set.\",\"PeriodicalId\":442608,\"journal\":{\"name\":\"Workshop on I/O in Parallel and Distributed Systems\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on I/O in Parallel and Distributed Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/236017.236047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on I/O in Parallel and Distributed Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/236017.236047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structured permuting in place on parallel disk systems
The ability to perform permutations of large data sets in place reduces the amount of necessary available disk storage. The simplest way to perform a permutation often is to read the records of a data set from a source portion of data storage, permute them in memory, and write them to a separate target portion of the same size. It can be quite expensive, however, to provide disk storage that is twice the size of very large data sets. Permuting in place reduces the expense by using only a small amount of extra disk storage beyond the size of the data set.