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一种使用改进预测成本矩阵任务优先排序的异构计算系统列表调度算法

姚宇 宋宇鲲 杨国伟 黄英 张多利

姚宇, 宋宇鲲, 杨国伟, 黄英, 张多利. 一种使用改进预测成本矩阵任务优先排序的异构计算系统列表调度算法[J]. 电子与信息学报, 2023, 45(1): 125-133. doi: 10.11999/JEIT211150
引用本文: 姚宇, 宋宇鲲, 杨国伟, 黄英, 张多利. 一种使用改进预测成本矩阵任务优先排序的异构计算系统列表调度算法[J]. 电子与信息学报, 2023, 45(1): 125-133. doi: 10.11999/JEIT211150
YAO Yu, SONG Yukun, YANG Guowei, HUANG Ying, ZHANG Duoli. A List Scheduling Algorithm for Heterogeneous Computing Systems Using Improved Predict Cost Matrix for Task Prioritizing[J]. Journal of Electronics & Information Technology, 2023, 45(1): 125-133. doi: 10.11999/JEIT211150
Citation: YAO Yu, SONG Yukun, YANG Guowei, HUANG Ying, ZHANG Duoli. A List Scheduling Algorithm for Heterogeneous Computing Systems Using Improved Predict Cost Matrix for Task Prioritizing[J]. Journal of Electronics & Information Technology, 2023, 45(1): 125-133. doi: 10.11999/JEIT211150

一种使用改进预测成本矩阵任务优先排序的异构计算系统列表调度算法

doi: 10.11999/JEIT211150
基金项目: 安徽高校协同创新项目(GXXT-2019-030)
详细信息
    作者简介:

    姚宇:男,博士生,研究方向为多核片上网络系统编译技术

    宋宇鲲:男,副研究员,研究方向为片上网络优化、多核系统设计、数字信号处理的VLSI实现

    杨国伟:女,硕士生,研究方向为可重构计算技术

    黄英:女,教授,博士生导师,研究方向为敏感材料与传感器技术

    张多利:男,研究员,博士生导师,研究方向为多核处理器体系架构和设计方法

    通讯作者:

    张多利 zhangduoli@hfut.edu.cn

  • 中图分类号: TN401

A List Scheduling Algorithm for Heterogeneous Computing Systems Using Improved Predict Cost Matrix for Task Prioritizing

Funds: The University Synergy Innovation Program of Anhui Province (GXXT-2019-030)
  • 摘要: 异构计算系统执行应用效率的提高高度依赖有效的调度算法。该文提出一种新的列表调度算法,称为改进的预测优先任务和乐观处理器选择调度(IPPOSS)。通过在任务优先级排序阶段引入任务的后向预测成本,来减少调度长度。与现有工作相比,该文使用改进预测成本矩阵(IPCM),更合理地进行了任务优先级排序,从而在处理器选择阶段获得了更好的解,并保持2次时间复杂度。IPCM考虑了任务优先级排序阶段的各种计算、通信因素,比预测优先任务调度(PPTS)提出的预测成本矩阵(PCM)更容易获得合理的优先级列表。随机生成应用的有向无环图(DAG)和真实世界应用的DAG的实验结果分析表明,IPPOSS的性能优于相关算法。
  • 图  1  具有10 任务和3 处理器的任务以及计算消耗

    图  2  4种算法对图1中DAG1产生的调度结果

    图  3  4种算法对不同任务数量的DAG的调度结果

    图  4  4种算法对不同ccr的DAG的调度结果

    图  5  4种算法对不同处理器数量的DAG的调度结果

    图  6  4种算法对不同矩阵尺寸的高斯消元应用的调度结果

    图  7  4种算法对不同任务数量的蒙太奇应用的调度结果

    表  1  计算消耗

    任务p1p2p3
    t16454
    t2131234
    t3452334
    t45334
    t531642
    t623547
    t7995634
    t8541344
    t9751387
    t10843237
    下载: 导出CSV

    表  2  图 1 中 DAG1 的 IPCM

    任务p1p2p3
    t1218242214
    t2156102177
    t3196174176
    t4130123129
    t5127106166
    t6178144162
    t7195120108
    t814977118
    t920477161
    t10843237
    下载: 导出CSV

    表  3  图 1 中 DAG1 的各算法任务优先级列表

    任务${\text{ran}}{{\text{k}}_\mu }$${\text{ran}}{{\text{k}}_{{\text{OCT}}}}$${\text{ran}}{{\text{k}}_{{\text{PCM}}}}$${\text{ran}}{{\text{k}}_{{\text{IPCM}}}}$
    t1325.0114.0224.0224.7
    t2247.080.3137.3145.0
    t3189.077.0182.0182.0
    t4211.368.3145.7127.3
    t5228.767.7128.7133.0
    t6212.078.7162.7161.3
    t7146.044.3122.7141.0
    t8119.044.097.7114.7
    t9174.351.0120.3147.3
    t1051.0051.051.0
    下载: 导出CSV

    表  4  不同方法的SLR对比(%)

    PEFTPPTSIPPOSS with EFTOCT
    更好57.5035.9016.30
    相同13.4029.9041.70
    更差29.1034.1042.00
    平均降低1.750.13–0.48
    下载: 导出CSV
    算法1 IPPOSS算法
     (1) 从出口任务到入口任务递归地为每个任务计算${\text{IPCM}}$和${\text{OCT}}$
     (2) 计算所有任务的 ${\text{ran}}{{\text{k}}_{{\text{IPCM}}}}$
     (3) 更新就绪列表
     (4) while 就绪列表非空 do
     (5)   ${{\mathbf{t}}_i} \leftarrow $就绪列表中具有最高 ${\text{ran}}{{\text{k}}_{{\text{IPCM}}}}({{\mathbf{t}}_i})$的任务
     (6)   for ${{\mathbf{p}}_m} \in {\mathbf{P}}$
     (7)     使用基于插入的策略计算${\text{EFT(}}{{\mathbf{t}}_i},{{\mathbf{p}}_m}{\text{)}}$
     (8)     计算$ {\text{EF}}{{\text{T}}_{{\text{OCT}}}}({{\mathbf{t}}_i},{{\mathbf{p}}_m}) $
     (9)   end for
     (10)   为${{\mathbf{t}}_i}$选择可以最小化$ {\text{EF}}{{\text{T}}_{{\text{OCT}}}}({{\mathbf{t}}_i},{{\mathbf{p}}_m}) $的${{\mathbf{p}}_m}$
     (11)   更新就绪列表
     (12) end while
    下载: 导出CSV

    表  5  4种算法的调度长度的成对比较(%)

    IPPOSSPPTSPEFTHEFT
    IPPOSS更好
    相同
    更差
    *44.9
    32.4
    22.7
    58.8
    26.0
    15.2
    66.7
    9.7
    23.5
    PPTS更好
    相同
    更差
    22.7
    32.4
    44.9
    *57.3
    11.5
    31.1
    63.5
    9.2
    27.3
    PEFT更好
    相同
    更差
    15.2
    26.0
    58.8
    31.1
    11.5
    57.3
    *54.2
    5.0
    40.8
    HEFT更好
    相同
    更差
    23.5
    9.7
    66.7
    27.3
    9.2
    63.5
    40.8
    5.0
    54.2
    *
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-21
  • 录用日期:  2022-01-20
  • 修回日期:  2022-01-12
  • 网络出版日期:  2022-02-03
  • 刊出日期:  2023-01-17

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