一种可证明良好的贪婪任务聚类启发式算法

M. Palis, Jing-Chiou Liou, David S. L. Wei
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引用次数: 4

摘要

针对具有重复(或重计算)的任务聚类问题,提出了一种简单的贪心算法,对于任意粒度的任务图,该算法产生的最大完工时间至多为两次最优的调度。此外,随着任务图粒度的增加,计划的质量也会提高。例如,如果粒度至少为1/2,则计划的makespan最多为最优的5/3倍。对于具有n个任务和e个任务间通信约束的任务图,该算法运行时间为O(n(nlgn +e)),比目前已知的解决该问题的算法快n倍。开发了类似的算法,产生:(1)粗粒度图的最优调度;(2)无任务重复树的2-最优调度;(3)无任务重复的粗粮树最优调度
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A greedy task clustering heuristic that is provably good
A simple greedy algorithm is presented for task clustering with duplication (or recomputation) which, for a task graph with arbitrary granularity, produces a schedule whose makespan is at most twice optimal. Furthermore, the quality of the schedule improves as the granularity of the task graph increases. For example, if the granularity is at least 1/2 , the makespan of the schedule is at most 5/3 times optimal. For a task graph with n tasks and e inter-task communication constraints, the algorithm runs in O(n(n lg n+e)) time, which is n times faster than the currently best known algorithm for this problem. Similar algorithms are developed that produce: (1) optimal schedules for coarse grain graphs; (2) 2-optimal schedules for trees with no task duplication; and (3) optimal schedules for coarse grain trees with no task duplication.<>
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