{"title":"负载平衡:程序员的方法或任务长度参数对并行程序负载平衡性能的影响","authors":"Y. Ben-Asher, A. Schuster, J. F. Sibeyn","doi":"10.1142/S0129053395000178","DOIUrl":null,"url":null,"abstract":"We consider the problem of dynamic load balancing in an n processor parallel system. The scheduling process of a parallel program is modeled by randomly throwing weighted balls into n holes. For a given program A, the ball weights (task lengths) are chosen according to a probability distribution , for which we know only some of the following parameters: the expectation μ, variance σ2, maximum M and minimum m. From these parameters, we derive an upper bound for the number of tasks to be generated by A in order to achieve a load balancing ratio for which the run-time is optimal up to a factor (1+e)2 for any 0<e≤0.5, with very high probability. Using the derived relations, the programmer may control the load-balancing of his program by tuning the global parameters of the generated tasks. This can be done regardless of the underlying scheduler used by the parallel machine. We also give experimental results of marine-life simulation in support of our claims.","PeriodicalId":270006,"journal":{"name":"Int. J. High Speed Comput.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Load Balancing: a Programmer's Approach or the Impact of Task-Length Parameters on the Load Balancing Performance of Parallel Programs\",\"authors\":\"Y. Ben-Asher, A. Schuster, J. F. Sibeyn\",\"doi\":\"10.1142/S0129053395000178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of dynamic load balancing in an n processor parallel system. The scheduling process of a parallel program is modeled by randomly throwing weighted balls into n holes. For a given program A, the ball weights (task lengths) are chosen according to a probability distribution , for which we know only some of the following parameters: the expectation μ, variance σ2, maximum M and minimum m. From these parameters, we derive an upper bound for the number of tasks to be generated by A in order to achieve a load balancing ratio for which the run-time is optimal up to a factor (1+e)2 for any 0<e≤0.5, with very high probability. Using the derived relations, the programmer may control the load-balancing of his program by tuning the global parameters of the generated tasks. This can be done regardless of the underlying scheduler used by the parallel machine. We also give experimental results of marine-life simulation in support of our claims.\",\"PeriodicalId\":270006,\"journal\":{\"name\":\"Int. J. High Speed Comput.\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. High Speed Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S0129053395000178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. High Speed Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0129053395000178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Load Balancing: a Programmer's Approach or the Impact of Task-Length Parameters on the Load Balancing Performance of Parallel Programs
We consider the problem of dynamic load balancing in an n processor parallel system. The scheduling process of a parallel program is modeled by randomly throwing weighted balls into n holes. For a given program A, the ball weights (task lengths) are chosen according to a probability distribution , for which we know only some of the following parameters: the expectation μ, variance σ2, maximum M and minimum m. From these parameters, we derive an upper bound for the number of tasks to be generated by A in order to achieve a load balancing ratio for which the run-time is optimal up to a factor (1+e)2 for any 0
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