{"title":"非异构多处理机系统中任务的节能分配","authors":"Chin-Fu Kuo, Yung-Feng Lu","doi":"10.1145/2663761.2663765","DOIUrl":null,"url":null,"abstract":"This paper aims to study the scheduling problem of a heterogeneous non-DVS multiprocessor platform with a task set. The processors have different characteristics of power consumption. We propose an off-line task-to-processor assignment algorithm, the Best-Fit Decreasing Physical Power Consumption (BDPC) algorithm to derive a feasible task assignment with the minimal energy consumption and has the time complexity of O(N(logN + M)), where N and M are the numbers of tasks and processor types, respectively. A series of experiments were conducted to evaluate the proposed algorithm. The experimental results demonstrate that the performance of the proposed BDPC algorithm is better than the compared algorithms.","PeriodicalId":120340,"journal":{"name":"Research in Adaptive and Convergent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Energy-efficient assignment for tasks on non-dvs heterogeneous multiprocessor system\",\"authors\":\"Chin-Fu Kuo, Yung-Feng Lu\",\"doi\":\"10.1145/2663761.2663765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to study the scheduling problem of a heterogeneous non-DVS multiprocessor platform with a task set. The processors have different characteristics of power consumption. We propose an off-line task-to-processor assignment algorithm, the Best-Fit Decreasing Physical Power Consumption (BDPC) algorithm to derive a feasible task assignment with the minimal energy consumption and has the time complexity of O(N(logN + M)), where N and M are the numbers of tasks and processor types, respectively. A series of experiments were conducted to evaluate the proposed algorithm. The experimental results demonstrate that the performance of the proposed BDPC algorithm is better than the compared algorithms.\",\"PeriodicalId\":120340,\"journal\":{\"name\":\"Research in Adaptive and Convergent Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in Adaptive and Convergent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2663761.2663765\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Adaptive and Convergent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2663761.2663765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
研究具有任务集的异构非分布式多处理机平台的调度问题。处理器的功耗有不同的特点。我们提出了一种离线任务-处理器分配算法,即最佳拟合降低物理功耗(Best-Fit reduction Physical Power Consumption, BDPC)算法,以最小的能量消耗得出可行的任务分配,其时间复杂度为O(N(logN + M)),其中N和M分别为任务数和处理器类型。通过一系列实验对该算法进行了验证。实验结果表明,所提出的BDPC算法的性能优于对比算法。
Energy-efficient assignment for tasks on non-dvs heterogeneous multiprocessor system
This paper aims to study the scheduling problem of a heterogeneous non-DVS multiprocessor platform with a task set. The processors have different characteristics of power consumption. We propose an off-line task-to-processor assignment algorithm, the Best-Fit Decreasing Physical Power Consumption (BDPC) algorithm to derive a feasible task assignment with the minimal energy consumption and has the time complexity of O(N(logN + M)), where N and M are the numbers of tasks and processor types, respectively. A series of experiments were conducted to evaluate the proposed algorithm. The experimental results demonstrate that the performance of the proposed BDPC algorithm is better than the compared algorithms.