一种并行结构路由器保序自适应负载平衡结构

论文提出了一种通用的并行结构路由器具有IP包保序功能的自适应负载平衡结构APOKLB(AdaptivePacket-Order-KeepingLoadBalancer)和一种有效的基于流分类的信元调度算法FCS(FlowClassification-basedScheduling)。分析了FCS的算法复杂度、APOKLB所需内存数量、带宽及其平均排队延迟,并给出了APOKLB与几种保序结构进行了比较。     

多处理机系统的自适应动态负载平衡算法研究

通过对多处理机系统中自适应动态负载平衡算法的分析研究,针对现有算法未考虑任务之间通信量及节点机可能出现故障等问题,对现有算法进行了改进,并给出了算法的主体思想。     

一种并行BP神经网络的动态负载平衡方案

为了加快在大规模神经网络训练下并行技术的训练速度问题，从BP算法的内部结构分析了BP神经网络算法的大规模行划分方法，提出了一种动态负载平衡方案。通过在PC集群环境下对并行算法的试验结果表明．这种并行划分提高了加速比，具有现实意义。     

一种并行BP神经网络的动态负载平衡方案

为了加快在大规模神经网络训练下并行技术的训练速度问题,从BP算法的内部结构分析了BP神经网络算法的大规模行划分方法,提出了一种动态负载平衡方案。通过在PC集群环境下对并行算法的试验结果表明,这种并行划分提高了加速比,具有现实意义。     

改进的分布式动态自适应负载平衡算法探讨

为了使分布式网络保持高效的运作,在多种常用的负载平衡算法中选择了动态自适应负载平衡算法,根据结点负载情况,区分轻载结点和重载结点。     

Euler方程非结构网格分布式并行计算

在二维非结构网格情形下,针对PC集群并行系统提出一种新的并行算法,在前景网格与背景网格之间建立起逻辑映射关系来交换边界计算数据并提高并行效率,该算法在应用中能有效地保持并行系统的负载平衡和边界计算数据的正确交换,多个数值算例的结果表明了该算法的有效性。     

并行微观交通动态负载平衡预测方法仿真

研究交通负载平衡优化预测问题,因交通负载量增多,导致车流的突变性呈现无规则瞬时增大.由于车辆分布不平衡,造成难以准确进行观测.传统的串行负载平衡方法面对忽然增加的调度任务,在很多情况下计算消耗时间将会超过实际运营时间,导致负载调度效果不佳.提出一种并行微观交通动态负载平衡预测方法,算法将仿真车辆作为各节点机负载平衡指标,当一台节点机上的负载需要向另一台节点机迁移时,从离子网中心节点最远的节点开始,保证负载迁移后每台节点机上的子网仍然是互连的,保证了速度.以TPSS并行仿真平台进行并行加速比性能和扩展性能测试分析.仿真结果表明,改进算法适用于较大规模的交通网络微观预测,能够获得较好的并行速度效果及预测准确性.     

一种有效的并行数据库动态负载平衡连接算法

在基于Shared-nothing结构的并行数据库中,负载平衡一直是影响查询处理性能的重要因素。在数据库中频繁使用的连接操作会因为各种因素导致的负载倾斜和额外的通讯开销而降低数据库的整体性能。提出了一种基于RCMD分布方法的动态负载平衡连接算法,能够在连接操作的执行过程中动态调整各个结点的负载。理论分析和实验结果证明提出的算法能够有效地平衡负载,提高并行数据库的执行效率。     

基于非结构滑移网格技术的旋翼型无人机空气动力学并行数值模拟方法

在现代飞行器设计中,数值模拟方法以低成本、高效率和高灵活性等优点成为研究飞行器空气动力学的重要方法.在旋翼型无人机流场模拟中,由于旋翼与机身存在相互作用,为获得精确模拟结果需要对整个无人机的流场进行模拟,因此,有效地模拟旋翼与机身的相对运动是实现成功模拟的关键步骤,这使得此类模拟问题极具挑战性.文章设计了一套求解旋翼型无人机空气动力学数值模拟问题的基于非结构滑移网格技术的高可扩展并行计算方法.该方法对控制方程的离散,在空间方向采用非结构移动网格有限元方法,时间推进采用全隐式二阶向后差分格式,最后采用一种并行Newton-Krylov-Schwarz方法求解离散后的非线性方程组.作为应用,文章对一个真实旋翼型无人机模型在悬停状态下的外流场进行了数值模拟,获得了一些非常详细的流场信息.数值结果显示,算法在天河2号上使用4 096个处理器核时仍具有接近线性的并行加速比,这为下一步开展旋翼型无人机的高保真度快速模拟奠定了良好的基础.     

动态碰撞网格的DSMC及其并行化

DSMC(Direct Simulate Monte Carlo)方法是处理稀薄气体问题的一种有效的方法,但现有的DSMC存在着网格生成及处理复杂、算例需进行大量人工调整、计算量大、耗时长等缺点.分析DSMC方法中对网格的要求以及网格在整个DSMC方法中所起的作用,提出了动态划分碰撞网格的DSMC算法,有效地解决了复杂流场条件下网格自适应的问题,并通过实验验证了该算法的正确性.同时,针对DSMC算法计算量大的特点,利用共享内存的并行模型对动态网格的DSMC算法进行了并行化,得到了较好的结果.     

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems.The objective is to minimize the total exectuion time of single applications.This paper has proposed an ARID strategy for distributed dynamic load balancing.Its principle and control protocol are described,and te communication overhead,the effect on system stability and the performance efficiency are analyzed.Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.     

基于时间偏差的并行逻辑模拟的动态负载平衡

随着大规模集成电路的复杂性日益增加,逻辑模拟开始采用并行离散事件模拟技术。在现有的基于时间偏差协议的并行逻辑模拟系统的基础上,提出了一个动态负载平衡模型,模型能够针对模拟时的负载变化,进行以一组模拟对象为单位的迁移以实现负载平衡。提出模拟推进度的概念,作为对并行逻辑模拟过程中的负载进行准确的衡量标准。     

An adaptive load balancing management technique for RFID middleware systems

In recent years, Radio Frequency Identification (RFID) industries have taken a great interest in utilizing the benefits of RFID for supply chain management, inventory control and various other applications. This paper proposed an adaptive load balancing technique for RFID middleware systems to meet the demands of scalability and heterogeneity. First, we explored five basic load balancing policies, namely, information policy, job selection policy, transfer policy, initiation policy and location policy. Eighteen load balancing schemes were then proposed for RFID middleware systems that were combinations of various types of the five basic load balancing policies. Our empirical study suggested that these load balancing strategies performed differently under different workload statuses. Finally, an adaptive load balancing strategy was proposed. The load balancing schemes and the proposed adaptive load balancing strategy have been implemented in the RFID Middleware Load Management System (RM‐LMS). Copyright © 2010 John Wiley & Sons, Ltd.     

A novel dynamic load balancing scheme for parallel systems

Adaptive mesh refinement (AMR) is a type of multiscale algorithm that achieves high resolution in localized regions of dynamic, multidimensional numerical simulations. One of the key issues related to AMR is dynamic load balancing (DLB), which allows large-scale adaptive applications to run efficiently on parallel systems. In this paper, we present an efficient DLB scheme for structured AMR (SAMR) applications. This scheme interleaves a grid-splitting technique with direct grid movements (e.g., direct movement from an overloaded processor to an underloaded processor), for which the objective is to efficiently redistribute workload among all the processors so as to reduce the parallel execution time. The potential benefits of our DLB scheme are examined by incorporating our techniques into a SAMR cosmology application, the ENZO code. Experiments show that by using our scheme, the parallel execution time can be reduced by up to 57% and the quality of load balancing can be improved by a factor of six, as compared to the original DLB scheme used in ENZO.     

Dynamic load balancing with adaptive factoring methods in scientific applications

To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed. Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular, methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing. The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently introduced adaptive factoring method.

Partitioning of unstructured meshes for load balancing

Many large-scale engineering and scientific calculations involve repeated updating of variables on an unstructured mesh. To do these types of computations on distributed memory parallel computers, it is necessary to partition the mesh among the processors so that the load balance is maximized and interprocessor communication time is minimized. This can be approximated by the problem of partitioning a graph so as to obtain a minimum cut, a well-studied combinatorial optimization problem. Graph partitioning is NP complete, so for real world applications one resorts to heuristics, i.e. algorithms that give good but not necessarily optimum solutions. These algorithms include recursive spectral bisection, local search methods such as Kernighan-Lin, and more general purpose methods such as simulated annealing. We show that a general procedure enables us to combine simulating annealing with Kernighan-Lin. The resulting algorithm is both very fast and extremely effective.     

基于时间偏差协议的动态负载平衡技术*

对并行VHDL模拟的特殊性进行分析后,建立了一个并行VHDL模拟的动态负载平衡模型。在此模型中,提出动态调节最佳并行规模的动态负载平衡方法来解决系统资源紧张的问题,采用一种新的模拟中负载的度量方法——模拟推进度。此模型还包括基于标准偏差和最小通信变化量的动态负载平衡算法和一个运行中的负载迁移机制。最后对该模型进行可行性分析。     

一种基于负载反馈的分布式数字集群动态负载均衡算法

针对传统负载均衡算法不能满足公网数字集群系统高并发用户请求和快速呼叫建立等需求,提出一种基于负载反馈的分布式数字集群动态负载均衡算法,实现公网数字集群系统负载均衡,提高用户容量。首先建立参与MCPTT服务器的静态负载和动态负载监控机制和指标;然后利用加权轮询算法为用户分配参与MCPTT服务器,并通过用户请求的处理获得复合负载参数;根据负载指标的反馈更新参与MCPTT服务器权值以动态调整服务器负载。仿真结果表明,该算法的负载均衡效果优于传统算法和其他动态反馈算法,负载均衡度更小、用户请求响应延迟更低。     

基于网格计算的混合负载均衡策略

根据分布式系统的静态和动态负载均衡策略的优缺点,提出了在网格计算环境下的混合负载均衡策略.为了让网络中节点在网格计算环境中有效地执行需要大量计算的复杂任务,提出了用来评估节点效率的函数,并结合模拟实验证实了在此函数下算法的优越性.