{"title":"Mapping Precedence-Constrained Simulation Tasks for a Parallel Environment","authors":"J. Sartor, G. Lamont, R. Hammell, T. Hartrum","doi":"10.1109/DMCC.1991.633067","DOIUrl":null,"url":null,"abstract":"The Mapping Problem Classical results on the deterministic precedence- constrained scheduling problem are almost exclusively concerned with a single iteration of the task system. This paper explores the problem of mapping deter- ministic tasks to processors in a parallel simulation environment, with each task iterating multiple times. Counterexamples are shown to demonstrate that mul- tiple passes through an optimal mapping for one iter- ation of a task system may produce less-than-optimal results when compared to mappings based on the it- erative nature of the simulation. A level strategy for assigning iterative tasks to processors is developed, and theoretical and experimental results are discussed for different mapping strategies in a VHDL simulation. This paper examines the classical multiprocessor scheduling problem for application to deterministic simulation systems. The tasks in these systems are characterized by iterative executions: each task exe- cutes more than once in the course of a simulation run. The general task scheduling problem and its relation- ship to the mapping problem for simulation tasks are introduced. The problem space is constrained, lim- iting the scope of the study to systems which map equal-execution time tasks into identical processors. A theoretical basis for the level strategy of iterative task assignment is summarized, and a polynomial- time algorithm based on this strategy is given. The results of hypercube experiments based on different mapping strategies are discussed with application to VHDL logic simulation.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DMCC.1991.633067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Mapping Problem Classical results on the deterministic precedence- constrained scheduling problem are almost exclusively concerned with a single iteration of the task system. This paper explores the problem of mapping deter- ministic tasks to processors in a parallel simulation environment, with each task iterating multiple times. Counterexamples are shown to demonstrate that mul- tiple passes through an optimal mapping for one iter- ation of a task system may produce less-than-optimal results when compared to mappings based on the it- erative nature of the simulation. A level strategy for assigning iterative tasks to processors is developed, and theoretical and experimental results are discussed for different mapping strategies in a VHDL simulation. This paper examines the classical multiprocessor scheduling problem for application to deterministic simulation systems. The tasks in these systems are characterized by iterative executions: each task exe- cutes more than once in the course of a simulation run. The general task scheduling problem and its relation- ship to the mapping problem for simulation tasks are introduced. The problem space is constrained, lim- iting the scope of the study to systems which map equal-execution time tasks into identical processors. A theoretical basis for the level strategy of iterative task assignment is summarized, and a polynomial- time algorithm based on this strategy is given. The results of hypercube experiments based on different mapping strategies are discussed with application to VHDL logic simulation.