一种多Agent系统相关任务的并行调度算法

本文研究多Agent系统(MAS)相关任务调度问题,从时间和空间两方面考虑,提出了一种新颖的多Agent 相关任务的并行调度算法--多Agent相关任务关联矩阵调度算法(Multi-Agent Dependent Tasks Correlation Matdx Scheduling Algorithm,MADTCMSA),利用可变的关联矩阵表示任务的时间需求与Agent的局部存储空间的关系及任务分配的状态.实验显示该算法具有最短或较短的调度长度,并且具有较好的时间均衡性和空间协调性.     

多机相关任务调度的优化策略与组织方法

本文提出了在有同一多机相关任务约束下,执行批量任务操作时,调度组织的最佳性概念和实现策略,证明了调度组织的最佳性准则,采用PDL语言提供了最佳调度组织算法,并与多机相关任务的改进性分枝定界调度算法进行比较,证明其最佳性更为完备。     

多机相关电网任务数据的动态分批优化调度算法

传统的动态分批优化调度算法对数据初始分析效果差,调度准确率较低,为解决上述问题,基于多机相关电网任务数据研究了一种新的动态分批优化调度算法,获取多机相关电网任务数据,对数据进行集中标定,保证数据处于系统可操作范围内,实现对数据的精准处理.实验结果表明,多机相关电网任务数据的动态分批优化调度算法能够很好地提高调度准确率,更精准地完成初始分析.     

一种无线网络控制系统的时空实时任务调度算法

提出了一种无线网络控制系统的时间空间混合优先级调度算法(STS)。该算法综合考虑了无线网络控制系统中机器人之间与任务区域的距离和任务有效截止期,将时间和距离两个因素通过多个步骤逐步量化,最终形成最优的优先级队列,移动机器人根据优先级队列逐个解决区域的任务需求。为了验证STS算法的性能,做了大量的仿真实验来证明时间空间混合优先级算法的优势。实验结果表明,在确保高优先级调度成功并提高系统稳定性的前提下,时间空间混合优先级算法在任务请求吞吐率、任务解决成功率和平均响应时间等方面与之前的算法相比都具有更好的调度表现。     

一种优化的多Agent相关任务并行调度算法

讨论了在多Agent系统中多组作业的并行调度问题，提出了一个描述多组作业推进速度的指标——调度效率和一种优化的多Agent相关任务并行调度算法——多Agent相关任务均衡 压缩调度算法(MADTBCSA)。以调度效率作为调度的标准，通过追求多组作业的均衡推进，来达到有效利用Agent时间的目的，同时利用静态压缩算法，进一步压缩调度长度，提高了Agent的利用率。     

基于任务复制的处理器预分配算法

基于任务复制的调度算法比无任务复制的调度算法具有较好的性能．文章在分析了基于任务复制的几个典型算法(如TDS，OSA等算法)及其假设条件后，提出了以使调度长度最短作为主要目标、减少处理机数目作为次要目标的处理器预分配算法PPA．该算法对任务计算时间与任务间通信时间未做任何限制(即不考虑任务粒度)．通过与相关工作的比较可以看出：PPA算法在调度长度与处理器使用数目上均优于其它算法或与其它算法相当，同时，该算法具有与TDS，OSA相同的时间复杂度．这对嵌入式实时分布系统具有重要的意义。     

多机Flow Shop加权完成时间调度问题的渐近最优算法研究

最近Chou、Queyranne和Simchi—Levi，Liu分别证明了恒速平行机调度问题和Flow shop调度问题的基于有效作业加权最短处理时间的启发式算法是渐近最优的。本文使用分组机器模型的方法证明：即使对于多机Flow shop加权完成时间调度问题，基于有效作业加权最短处理时间的启发式算法也是渐近最优的。关键词调度，多机Flow shop调度，启发式算法，渐近最优分析     

调度PVM任务

本文介绍了一个自行设计和实现的ＰＶＭ任务调度系统，可进行空间机选择，对任务池和动态生成的子任务进行调度，提高处理机资源利用率，改进作业响应时间和简化用户编程过程。     

嵌入式系统中BP算法多任务调度性能的分析

对于多任务、多进程实时系统中的周期性任务,有一系列静态分配调度算法能有效地解决各种特定条件下的任务分配和调度问题,但这些算法均要求被调度任务的特征参数为已知条件,在很多实时系统中,周期性任务的运行时间或任务数量常常是一些具有一定规律的随机过程,上述静态算法的效能将受到限制。该文描述的神经网络能够充分利用不同时间和空间的数据信息,有较强的学习功能,提高了系统的性能和效率。     

多机异构相关任务集的调度优化研究

本文首先介绍了异构相关任务集的概念,在实现子任务优化调度的前提下,基于相同处理机(identical processors)并根据多目标优化策略,提出了AOSA调度算法,构成多机相关任务集的优化调度方法。实验表明:(1)AOSA方法优于随机非AOSA方法对异构任务集的调度;(2)理论准则与实验结果一致。     

Using processor affinity in loop scheduling on shared-memorymultiprocessors

Loops are the single largest source of parallelism in many applications. One way to exploit this parallelism is to execute loop iterations in parallel on different processors. Previous approaches to loop scheduling attempted to achieve the minimum completion time by distributing the workload as evenly as possible while minimizing the number of synchronization operations required. The authors consider a third dimension to the problem of loop scheduling on shared-memory multiprocessors: communication overhead caused by accesses to nonlocal data. They show that traditional algorithms for loop scheduling, which ignore the location of data when assigning iterations to processors, incur a significant performance penalty on modern shared-memory multiprocessors. They propose a new loop scheduling algorithm that attempts to simultaneously balance the workload, minimize synchronization, and co-locate loop iterations with the necessary data. They compare the performance of this new algorithm to other known algorithms by using five representative kernel programs on a Silicon Graphics multiprocessor workstation, a BBN Butterfly, a Sequent Symmetry, and a KSR-1, and show that the new algorithm offers substantial performance improvements, up to a factor of 4 in some cases. The authors conclude that loop scheduling algorithms for shared-memory multiprocessors cannot afford to ignore the location of data, particularly in light of the increasing disparity between processor and memory speeds     

Dynamic task scheduling using online optimization

Algorithms for scheduling independent tasks on to the processors of a multiprocessor system must trade-off processor load balance, memory locality, and scheduling overhead. Most existing algorithms, however, do not adequately balance these conflicting factors. This paper introduces the self-adjusting dynamic scheduling (SADS) class of algorithms that use a unified cost model to explicitly account for these factors at runtime. A dedicated processor performs scheduling in phases by maintaining a tree of partial schedules and incrementally assigning tasks to the least-cost schedule. A scheduling phase terminates whenever any processor becomes idle, at which time partial schedules are distributed to the processors. An extension of the basic SADS algorithm, called DBSADS, controls the scheduling overhead by giving higher priority to partial schedules with more task-to-processor assignments. These algorithms are compared to two distributed scheduling algorithms within a database application on an Intel Paragon distributed memory multiprocessor system.     

基于动态关键任务的多处理器任务分配算法

多处理器调度问题是影响系统性能的关键问题,基于任务复制的调度算法是解决多处理器调度问题较为有效的方法.文中分析了几个典型的基于任务复制算法,提出了基于动态关键任务(DCT)的多处理器任务分配算法.DCT算法以克服贪心算法不足为要点,调度过程中动态计算任务时间参数,准确确定处理器的关键任务,以关键任务为核心优化调度,逐步改善调度结果,最终取得最优的调度结果.分析和实验证明,DCT算法优于现有其它同类算法.     

开销敏感的多处理器最优节能实时调度算法

嵌入式多处理器系统的能耗问题变得日益重要,如何减少能耗同时满足实时约束成为多处理器系统节能实时调度中的一个重要问题.目前绝大多数研究基于关键速度降低处理器的频率以减少动态能耗,采用关闭处理器的方法减少静态能耗.虽然这种方法可以实现节能,但是不能保证最小化能耗.而现有最优的节能实时调度未考虑处理器状态切换的时间和能量开销,因此在切换开销不可忽视的实际平台中不再是最优的.文中针对具有独立动态电压频率调节和动态功耗管理功能的多处理器系统,考虑处理器切换开销,提出一种基于帧任务模型的最优节能实时调度算法.该算法根据关键速度来判断系统负载情况,确定具有最低能耗值的活跃处理器个数,然后根据状态切换开销来确定最优调度序列.该算法允许实时任务在处理器之间任意迁移,计算复杂度小,易于实现.数学分析证明了该算法的最优性.     

Multiprocessors should support simple memory consistency models

In the future, many computers will contain multiple processors, in part because the marginal cost of adding a few additional processors is so low that only minimal performance gain is needed to make the additional processors cost effective. Intel, for example, now makes cards containing four Pentium Pro processors that can easily be incorporated into a system. Multiple processor cards like Intel's will help multiprocessing spread from servers to the desktop. But how will these multiprocessors be programmed? The evolution of the programming model is already under way. One important function of the programming model is to describe how memory operates. For a multiprocessor, a reasonable model is sequential consistency (SC), which makes a multiprocessor behave like a multitasking uniprocessor. Nevertheless, many commercial multiprocessors support more relaxed memory models. The author argues that multiprocessors should support SC because-with speculative execution, relaxed models do not provide sufficient additional performance to justify exposing their complexity to the authors of low level software     

Exploiting thread-level parallelism in the iterative solution of sparse linear systems

We investigate the efficient iterative solution of large-scale sparse linear systems on shared-memory multiprocessors. Our parallel approach is based on a multilevel ILU preconditioner which preserves the mathematical semantics of the sequential method in ILUPACK. We exploit the parallelism exposed by the task tree corresponding to the nested dissection hierarchy (task parallelism), employ dynamic scheduling of tasks to processors to improve load balance, and formulate all stages of the parallel PCG method conformal with the computation of the preconditioner to increase data reuse. Results on a CC-NUMA platform with 16 processors reveal the parallel efficiency of this solution.     

一种动态可重构系统的实时任务调度算法

硬件任务的软实时调度是影响动态可重构系统性能的关键因素之一。本文提出了一种基于顶点链表的硬件任务间最小空间调度算法MSSA,该算法将硬件任务按照长、宽及调度时间构成一个三维资源模型,以到达任务与已放置任务在三维空间的邻接度来构建代价函数,获取具有最大代价函数值的放置位置和启动时间,可使任务安排得更紧凑,减小对系统资源的浪费,提高并行度。仿真实验表明,与MSG-4V和Stuffing算法相比,本文算法具有更高的芯片利用率和任务接受率。     

基于多核处理器的关联任务并行感知调度算法

关联任务在多核处理器上并行调度所产生的通信时延,会对任务调度长度和处理器利用率造成负面影响,为了改善多核系统对关联任务的处理性能,针对关联任务在多核处理器上的调度特点,提出一种并行感知调度算法。计算各任务与终点间的最长路径值,按照该值的降序来分配任务调度次序,在分配处理器内核时兼顾关联度和任务最早可执行时间,设置最佳匹配评价函数。实验结果表明,与busHEFT和DTSV算法相比,该算法具有更短的任务调度时延、更少的通信量以及更高的处理器利用率。     

A flexibility coupled hypercube multiprocessor for high level vision

In general, message passing multiprocessors suffer from communication overhead between processors and shared memory multiprocessors suffer from memory contention. Also, in computer vision tasks, data I/O overhead limits performance. In particular, high level vision tasks, which are complex and require nondeterministic communication, are strongly affected by these disadvantages. This paper proposes a flexibly (tightly/loosely) coupled hypercube multiprocessor (FCHM) for high level vision to alleviate these problems. A variable address space memory scheme in which a set of adjacent memory modules can be merged into a shared memory module by a dynamically partitionable hypercube topology is proposed. The architecture is quantitatively analyzed using computational models and simulated on the Intel’s Personal SuperComputer (iPSC/I), a hypercube multiprocessor. A parallel algorithm for exhaustive search is simulated on FCHM using the iPSC/I showing significant performance improvements over that of the iPSC/I. This research was supported in part by IBM corporation.