An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches.     

Scheduling precedence constrained task graphs with non-negligibleintertask communication onto multiprocessors

The multiprocessor scheduling problem is the problem of scheduling the tasks of a precedence constrained task graph (representing a parallel program) onto the processors of a multiprocessor in a way that minimizes the completion time. Since this problem is known to be NP-hard in the strong sense in all but a few very restricted eases, heuristic algorithms are being developed which obtain near optimal schedules in a reasonable amount of computation time. We present an efficient heuristic algorithm for scheduling precedence constrained task graphs with nonnegligible intertask communication onto multiprocessors taking contention in the communication channels into consideration. Our algorithm for obtaining satisfactory suboptimal schedules is based on the classical list scheduling strategy. It simultaneously exploits the schedule-holes generated in the processors and in the communication channels during the scheduling process in order to produce better schedules. We demonstrate the effectiveness of our algorithm by comparing with two competing heuristic algorithms available in the literature     

基于多核处理器并行系统的任务调度算法

针对多核处理器并行系统的特点,提出了相应的任务调度算法,该算法在任务调度之前加入了任务分配技术,通过合理的任务分配,可有效减少多个处理器间的通信开销,使任务调度效率更佳.仿真实现了该算法,并通过实验数据证明了该算法的优越性.     

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。     

Task graph pre-scheduling, using Nash equilibrium in game theory

Prescheduling algorithms are targeted at restructuring of task graphs for optimal scheduling. Task graph scheduling is a NP-complete problem. This article offers a prescheduling algorithm for tasks to be executed on the networks of homogeneous processors. The proposed algorithm merges tasks to minimize their earliest start time while reducing the overall completion time. To this end, considering each task as a player attempting to reduce its earliest time as much as possible, we have applied the idea of Nash equilibrium in game theory to determine the most appropriate merging. Also, considering each level of a task graph as a player, seeking for distinct parallel processors to execute each of its independent tasks in parallel with the others, the idea of Nash equilibrium in game theory can be applied to determine the appropriate number of processors in a way that the overall idle time of the processors is minimized and the throughput is maximized. The communication delay will be explicitly considered in the comparisons. Our experiments with a number of known benchmarks task graphs and also two well-known problems of linear algebra, LU decomposition and Gauss–Jordan elimination, demonstrate the distinguished scheduling results provided by applying our algorithm. In our study, we consider ten scheduling algorithms: min–min, chaining, A ^?, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, DSH with task duplication, and our proposed algorithm (PSGT).     

一个新的基于通信竞争的任务调度算法

任务调度是并行处理的一个非常关键的方面。目前的调度算法大多假定处理器完全互连、可以并行接收数据,而没有考虑实际应用中通信链路的竞争及延迟,进而导致调度算法在具体应用中效率较低。论文研究异构计算环境下具有依赖关系任务的边调度问题,结合传统任务调度问题中的有效策略,提出一种新的调度算法,该算法通过串行化通信边使通信竞争集成化。实验结果表明,与各种经典调度方案相比,该算法显著地改善了精确性和效率。     

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the task’s priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.     

Optimal task scheduling algorithm for cyclic synchronous tasks in general multiprocessor networks

We develop an optimal task allocation and scheduling algorithm which minimizes the computing period for multiprocessor systems with general network structures considering task execution time and communication contentions and routing delays explicitly. We presented new ideas of scheduling: (i) individual start allowing overlapping two different iterations, (ii) the scheduling space and the scheduling graph representing feasible schedules, and (iii) the check-and-diffusion algorithm utilizing property of the start-time difference vs. the computing period. With concrete examples of scheduling spaces, segments, and schedules for various multiprocessor network architectures, we showed that individual start reduces the computing period, and our algorithm can find the optimal computing period without exhaustive search.     

Partitioning and mapping in embedded multiprocessor architectures in the presence of constraints

The paper focuses on the problem of partitioning and mapping parallel programs onto heterogeneous embedded multiprocessor architectures for real-time applications. Such applications present unique constraints and challenges. In addition to heterogeneity, the proposed partitioning and mapping algorithms satisfy memory, task throughput, task placement, intertask communication bandwidth, and co-location constraints. They do so for architectures that utilize circuit-switched (rather than packet-switched) interprocessor communication and optimize latency and throughput in addition to load-balancing. Finally, these mapping algorithms make use of knowledge of the local scheduling discipline to accommodate real-time scheduling constraints. Our focus is on unstructured parallel programs that fall into one of two classes: (i) the class of computations characteristic of control applications in a real-time environment where tasks execute concurrently, periodically exchanging information, and (ii) pipelined computation graphs found in sensor data processing applications. The algorithms are implemented in a set of tools that operate with commercial CASE tools at one end, and present an interface to multiprocessor simulators at the other end. Collectively, the algorithms form a significant component of an interactive design environment for the development and mapping of real-time embedded parallel programs. The paper describes the algorithms, the encapsulating toolset, and presents an example of their application to an existing embedded application—an Autonomous Underwater Vehicle application.     

非对称多核处理器上的操作系统集成调度

相对于对称多核处理器,非对称多核处理器具有更高的效能,将成为未来并行操作系统中的主流体系结构.对于非对称多核处理器上操作系统的并行任务调度问题,现有的研究假设所有核心频率恒定,缺乏理论分析,也没有考虑算法的效能和通用性.针对该问题,该文首先建立非线性规划模型,分析得出全面考虑并行任务同步特性、核心非对称性以及核心负载的调度原则.然后,基于调度原则提出一个集成调度算法,该算法通过集成线程调度和动态电压频率调整来提高效能,并通过参数调整机制实现了算法的通用性.提出的算法是第一个在非对称多核处理器上结合线程调度和动态电压频率调整的调度算法.实际平台上的实验表明:该算法可适用于多种环境,且效能比其他同类算法高24%～50%.     

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。     

满足偏序约束的在线调度

具备偏序关系的实时调度要求调度算法产生的执行序列既要满足任务的实时约束,又要满足任务间执行的偏序约束。基于并行拓扑排序,提出一种新的在线调度算法,该算法通过同时考察任务间执行的串行性和并行性来进行优先级设置,能够处理释放时间任意的任务集。给出该算法的原理和设计,并通过示例分析和比较对算法进行验证。     

云计算平台中非定期任务并行调度仿真

由于云计算平台的动态不确定性和非定期任务调度本身的复杂性,使得非定期任务调度过程中的耗时长和负载不均等问题很难得到有效解决.针对上述问题,提出一种非定期任务并行调度方法,并应用到云计算中.通过多方面考虑云平台客户非定期任务的截止时间底线、调度估算等并行调度约束条件和各种可用资源的性能参数,对非定期任务调度的多目标约束条...     

一种支持任务合并的交换网络实时调度策略

通过分析现有网络通信和实时系统的调度算法,在实时调度算法LSF(Least Start First)的基础上,提出支持任务合并的交换式网络实时调度策略TC-LSF(Tasks Combining-Least Start First)来保证任务在网络通信中的实时性.该算法使用任务合并策略对多个通信任务进行合并,从而节省相...     

Optimal scheduling of cooperative tasks in a distributed systemusing an enumerative method

Preemptive (resume) scheduling of cooperative tasks that have been preassigned to a set of processing nodes in a distributed system, when each task is assumed to consist of several modules is discussed. During the course of their execution, the tasks communicate with each other to collectively accomplish a common goal. Such intertask communications lead to precedence constraints between the modules of different tasks. The objective of this scheduling is to minimize the maximum normalized task response time, called the system hazard. Real-time tasks and the precedence constraints among them are expressed in a PERT/CPM form with activity on arc (AOA), called the task graph (TG), in which the dominance relationship between simultaneously schedulable modules is derived and used to reduce the size of the set of active schedules to be searched for an optimal schedule. Lower-bound costs are estimated, and are used to bound the search. An example of the task scheduling problem and some computational experiences are presented     

An Optimization-Based Approach for Design Project Scheduling

Concurrent engineering has been widely used in managing design projects to speed up the design process by concurrently performing multiple tasks. Since the progress of a design task often depends on the knowledge about other tasks and requires effective communication, tasks and communication activities need to be properly coordinated to avoid delays caused by waiting for information or the need for rework. This paper presents a novel formulation for design project scheduling with explicit modeling of task dependencies and the associated communication activities. General dependencies are modeled as combinations of three basic types representing sequential, concurrent, and independent processes. Communication activities are also modeled as tasks, and their interactions with design tasks are described by sets of intertask constraints. The objective is to achieve timely project completion with limited resources. To improve algorithm convergence and schedule quality, penalties on the violation of constraints coupling design tasks are added to the objective function. A solution methodology that combines Lagrangian relaxation, dynamic programming, and heuristic is developed to schedule design and communication tasks, and a surrogate optimization framework is used to overcome the “inseperability” caused by nonadditive penalties. A heuristic procedure is then developed to obtain scheduling policies from optimization results and to dynamically construct schedules. Numerical results show that the approach is effective to handle various task dependencies and the associated communication activities to provide high-quality schedules.      

Aviation maintenance technician scheduling with personnel satisfaction based on interactive multi-swarm bacterial foraging optimization

This study focuses on the challenges of aviation maintenance technician (AMT) scheduling and constructs a model based on personnel satisfaction and the parallel execution of aircraft maintenance tasks. To obtain the scheduling scheme from the constructed NP-hard model, an interactive multi-swarm bacterial foraging optimization (IMSBFO) algorithm is proposed using multi-swarm coevolution, structural recombination, and three information interactive mechanisms among individuals. Moreover, considering the distributed feature of the AMT scheduling problem, a specific mechanism is designed to convert continuous solution to a binary AMT scheduling scheme. Finally, a series of comparative experiments highlight the efficiency and superiority of our proposed IMSBFO algorithm, and the optimal scheduling scheme owns the delicate balance between the work and rest time.     

Combined task and message scheduling in distributed real-timesystems

The paper presents an algorithm for offline scheduling of communicating tasks with precedence and exclusion constraints in distributed hard real time systems. Tasks are assumed to communicate via message passing based on a time bounded communication paradigm, such as the real time channel (D.D. Kandlur et al., 1994). The algorithm uses a branch-and-bound (B & B) technique to search for a task schedule by minimizing maximum task lateness (defined as the difference between task completion time and task deadline), and exploits the interplay between task and message scheduling to improve the quality of solution. It generates a complete schedule at each vertex in the search tree, and can be made to yield a feasible schedule (found before reaching an optimal solution), or proceed until an optimal task schedule is found. We have conducted an extensive simulation study to evaluate the performance of the proposed algorithm. The algorithm is shown to scale well with respect to system size and degree of intertask interactions. It also offers good performance for workloads with a wide range of CPU utilizations and application concurrency. For larger systems and higher loads, we introduce a greedy heuristic that is faster but has no optimality properties. We have also extended the algorithm to a more general resource-constraint model, thus widening its application domain     

FF现场总线系统中实时任务的建模与调度

本文研究了单网段 FF现场总线系统中具有时间约束和次序约束的实时任务，即 功能块任务和通信任务的建模与调度．首先，将功能块任务和通信任务等视为相同的任务， 在只考虑任务间次序约束的情况下，提出了基于紧凑模式的任务模型，以保证每个作业被尽 可能早地完成．其次，考虑单网段通信任务共享一个传输介质而引起的通信超时，提出了基 于作业速率单调优先级算法的扩展紧凑模式的任务调度算法，以满足实时任务的时间约束 和次序约束．最后，通过一个应用实例来描述实时任务的调度过程．     

Scheduling algorithms for parallel Gaussian elimination withcommunication costs

We consider a graph theoretical model and study a parallel implementation of the well-known Gaussian elimination method on parallel distributed memory architectures, where the communication delay for the transmission of an elementary data is higher than the computation time of an elementary instruction. We propose and analyze two low-complexity algorithms for scheduling the tasks of the parallel Gaussian elimination on an unbounded number of completely connected processors. We compare these two algorithms with a higher-complexity general-purpose scheduling algorithm, the DSC heuristic, proposed by A. Gerasoulis and T. Yang (1993)