Efficient grouping-based mapping and scheduling on heterogeneous cluster architectures

Heterogeneous clusters of computers usually provide high computing power for large-scale applications at the expense of large cost. And there are two challenges currently faced by researchers. One is how to map large applications, modeled by Directed Acyclic Graphs (DAG), to heterogeneous architectures with minimal cost. The other is how to schedule tasks on each cluster to further decrease the total cost.     

Scenario-based quasi-static task mapping and scheduling for temperature-efficient MPSoC design under process variation

Nowadays, employing the worst case analysis is the most common approach to provide unified static task mapping–scheduling plans on MPSoCs. Since the whole design space nor a subset of design space are not explored in the worst case methods, these approaches may fail to achieve efficient performance yield. In this paper, we present a temperature-aware quasi-static task mapping–scheduling framework under process variation for hard real-time and periodic systems on MPSoCs. By employing the stochastic optimization and scenario-based approaches, we explore a few representative scenarios in the whole design space of the chip using the probability density function of the problem random variables. Then, we obtain a compact set of near optimal mapping–scheduling of real-time tasks which targets performance-yield maximization and minimization of the expected values of peak temperature. Consequently, considering different chip parameter configurations, we construct the plan set as the solutions that attain the best variation-aware task mapping–scheduling that satisfy the deadline and minimize the temperature. This plan set can readily look up at run time by the system scheduler of the chip to find the proper plan of the tasks based on the run-time parameters. The experimental results demonstrate significant improvements in performance-yield and peak temperature for almost all of the test cases off homogenous and heterogeneous MPSoCs.     

Mapping of option pricing algorithms onto heterogeneous many-core architectures

The rapid development of technologies and applications in recent years poses high demands and challenges for high-performance computing. Because of their competitive performance/price ratio, heterogeneous many-core architectures are widely used in high-performance computing areas. GPU and Xeon Phi are two popular general-purpose many-core accelerators. In this paper, we demonstrate how heterogeneous many-core architectures, powered by multi-core CPUs, CUDA-enabled GPUs and Xeon Phis can be used as an efficient computational platform to accelerate popular option pricing algorithms. In order to make full use of the compute power of this architecture, we have used a hybrid computing model which consists of two types of data parallelism: worker level and device level. The worker level data parallelism uses a distributed computing infrastructure for task distribution, while the device level data parallelism uses both the multi-core CPUs and many-core accelerators for fast option pricing calculation. Experiments show that our implementations achieve good performance and scalability on this architecture and also outperform other state-of-the-art GPU-based solutions for Monte Carlo European/American option pricing and BSDE European option pricing.     

异构集群下的任务调度算法研究

针对异构集群下高效节能的任务调度算法进行了研究, 提出了一种基于复制的任务调度算法, 在任务初始分配的基础上, 分别从能源感知和性能—能源平衡两个角度考虑任务的复制。建立了由计算和通信造成的能源消耗的数学模型, 并进行了大量的实验。实验结果表明, 与已有的BEATA算法相比, 该算法能明显地减少异构集群处理并行应用的调度长度和能耗。分析结果发现, 任务复制的方法在减少调度长度的同时会增加相应的能耗, 能同比优化调度长度和能耗的任务调度方法是今后的研究方向。     

异构系统中的综合性启发式任务调度算法

任务的单个属性常作为基于优先驱动的表调度算法的优先级,针对这种方法常出现优先级相同的情况,提出一个综合性启发式算法HCPFS。算法分三个优先级选择任务进行调度,从高到低依次为：关键路径上的任务、就绪任务到出口任务的路径长度和后继任务数。调度过程中,算法采用任务复制和空闲时间区段任务插入的方法。采用随机生成图法和任务图集进行了算法模拟和比较,实验数据表明HCPFS算法具有更好的调度性能。     

改进的异构多处理器的实时任务调度算法研究*

现有的很多调度算法存在时间复杂度过高或调度成功率低的问题。提出一种新的调度算法(HRTSA),提高实时任务的调度成功率。HRTSA首先通过METC策略初始化分簇,降低算法的时间复杂度;再在放置任务时根据处理器的负载均衡进行处理器负载的有效控制;最后通过任务复制调度以提高任务调度成功率。对比实验分析表明提出的HRTSA算法时间复杂度与RTSDA相比较低,调度成功率较高。     

异构多核系统任务调度算法研究

为解决异构多核系统任务调度问题,提出一种混合静态调度算法——HSCGS (hybrid successor concerned genetic scheduling),该算法分为启发式算法和遗传算法2个阶段.第1阶段采用所提出的考虑后继节点的列表启发式调度算法(SCLS)产生一个近似最优的调度结果;第2阶段采用针对调度问题改进的遗传算法IGA (improved genetic algorithm),对第1阶段产生的调度结果进行优化.将SCLS与StarPU相结合,实现一种动态调度算法——DSCLS(dynamic successor concerned list scheduling),通过与StarPU上已有调度算法的对比实验表明了DSCLS算法在运行时间和系统吞吐量两方面的优势.     

HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce

Distributed and Parallel Databases - As one of the most popular parallel data processing models, data analysis system MapReduce has been widely used in many fields. Task scheduling is the core...     

A low-power task scheduling algorithm for heterogeneous cloud computing

The Journal of Supercomputing - As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been...     

Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.     

A hybrid list-based task scheduling scheme for heterogeneous computing

The Journal of Supercomputing - Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task...     

异构环境下MapReduce动态任务调度技术研究

针对MapReduce在异构环境下各节点性能不均衡,导致整体计算效率低下的问题进行了研究。为此,从节点与任务两方面入手,提出了一种将节点性能量化并排序与将任务按相似度划分相结合的算法。该方法首先根据历史日志以及实时回传的日志信息将节点按照性能高低排序;然后根据任务执行完成的信息,将其与新任务进行比对得到相似度,从而推测出新任务的执行时间,执行时间长的认为是复杂的任务;最后进行动态调度,使高性能节点处理更复杂的任务。在随机生成数据集上的实验结果表明,所提出的动态调度算法与默认调度算法相比,数据集为20G大小时执行速度提高27.4%,数据集为100G大小时执行速度提高了74.1%。     

异构系统中针对通信密集型任务调度的算法*

针对异构多核系统的通信密集型任务的调度,提出了一种综合算法即,HCDUL。该算法首先进行聚簇,然后优先调度就绪关键路径节点,其他就绪节点根据节点的ranku值排序,当处理器有空闲时复制上层节点。使用随机生成图法进行模拟实验,与其他算法相比,HCDUL算法有更小的调度长度。     

Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

The Journal of Supercomputing - Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and...     

基于异构多核处理器的静态任务调度研究

针对现存任务调度算法优先级选取过于单一、冗余任务处理较晚的问题,提出一种基于加权优先级的任务调度算法--WPTS算法.该算法综合考虑任务3个属性的加权值以决定任务被处理的先后次序,从而克服了任务选取时的单一性问题.在将任务分配到处理器的过程中,保证任务优先调度到完成时间最早的处理器上.同时,引入冗余任务处理过程,及时消除冗余任务,达到对处理器空闲时间段进行有效回收、减少处理器调度长度的效果.性能对比实验表明,WPTS算法较CPFD算法、HCPFD算法和HDEFT算法能取得更好的性能.     

异构环境中Fork-Join任务图的调度算法

目前已有的Fork-Join任务图的调度算法大多假定处理机为同构的,而没有考虑实际应用中处理机的异构性以及节省处理机的问题,导致算法在具体应用中效率较低.因此,对Fork-Join任务图的调度问题进行研究,提出了一个基于异构环境的贪心调度算法,该算法具有高的加速比和总体效率,其时间复杂度为O(v~2),其中,v表示任务集中任务的个数.实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

异构系统中一种基于可用性的抢占式任务调度算法*

针对大多数现有的异构系统调度算法没有考虑由多类任务特别是抢占式任务所引起的可用性需求的不足,在现有基于可用性的非抢占式任务调度算法的基础上,通过计算任务的平均等待时间来确定优先级等级,对异构系统中多类抢占式任务的可用性约束的调度问题进行了探索,提出了一种基于可用性的抢占式优先调度算法P-SSAC。该算法在不增加硬件代价的前提条件下通过调度增加了系统的可用性,缩短了任务的平均等待时间,同时该算法可对抢占式的任务进行有效调度。仿真实验结果表明,该算法有效实现了异构系统可用性和任务等待时间之间的折中。     

An efficient list-based task scheduling algorithm for heterogeneous distributed computing environment

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.     

CPU-GPU异构多核系统的动态任务调度算法

CPU-GPU异构多核系统对计算密集型的应用加速效果显著而得到广泛应用,但该系统易出现负载均衡问题。针对此问题,本文提出了一种CPU-GPU异构多核系统的动态任务调度算法。该算法充分利用CPU的线程资源和GPU的计算资源,准确测量CPU和GPU的计算能力,从而动态调整分配到CPU和GPU上的数据块大小,减小负载的总执行时间,提高系统加速比。实验结果表明,该算法使得系统加速比提高34%~103%。