基于遗传算法的并行网络模拟拓扑划分

要提高并行网络模拟性能,需对网络模拟拓扑进行有效划分。为此,提出一种并行网络模拟拓扑的优化划分方法。分析影响并行网络模拟性能因素,给出并行网络模拟性能估计模型,以该模型为评价函数,采用遗传算法寻找优化划分,实现并行网络模拟拓扑的优化划分。在PDNS上的实验结果表明,与传统划分方法相比,该优化划分方法的并行模拟性能平均提高13.3%。     

多选择软硬件划分问题的计算模型与动态规划算法

软硬件划分是软硬件协同设计的关键环节,划分的结果直接影响目标系统的设计质量。因此,对于一个给定的应用程序,为了使得目标系统快速执行且成本低廉,合理的划分策略十分重要。由于单个任务具有多种不同的硬件实现方式,与传统的单一硬件实现方式的软硬件划分问题相比,多选择的软硬件划分更能客观地反映现实应用。这导致问题的求解更具挑战性,它们已被证明是NP完全问题。基于多核处理器片上系统并针对任务图为二叉树的应用,建立了多选择软硬件划分问题的计算模型,并提出了解决该问题的动态规划算法。实验结果表明,当问题规模适中时,所提动态规划算法能够有效地获得精确解,并展示了算法的计算能力与硬件面积限制之间的关系。     

在两个级别上对嵌入式系统应用进行硬件/软件划分

利用源代码级别的高级信息和二进制级别上性能准确估计的特点,在两个级别上对嵌入式系统进行硬件/软件划分,它支持动态划分,对用户透明;给出了系统划分模型,使用执行时间最少的增益函数启发下一步动作的启发算法。从实验结果可以看出,用较少的硬件面积基本不影响执行时间,特别地,在一些例子中执行时间还会有所提高。     

一种改进的并行保存划分算法

在计算需求量很大的电路模拟中,经常使用并行模拟技术来减少模拟时间。并行模拟中的划分被认为是提高模拟性能的有效途径。本文研究了针对并行逻辑模拟的一种优秀的启发式多层次划分算法——并行保存划分算法,针对其缺点做出了一些改进,得到一种新的划分算法。并将这种算法放到并行逻辑模拟框架SAVANT／TYVIS／WARPED中进行了验证,得到了较好的结果。     

Partitioning approach oriented to the decentralised predictive control of large-scale systems

In this paper, a partitioning approach for large-scale systems based on graph-theory is presented. The algorithm starts with the translation of the system model into a graph representation. Once the system graph is obtained, the problem of graph partitioning is then solved. The resultant partition consists in a set of non-overlapping subgraphs whose number of vertices is as similar as possible and the number of interconnecting edges between them is minimal. To achieve this goal, the proposed algorithm applies a set of procedures based on identifying the highly connected subgraphs with balanced number of internal and external connections. In order to illustrate the use and application of the proposed partitioning approach, it is used to decompose a dynamical model of the Barcelona drinking water network (DWN). Moreover, a hierarchical-like DMPC strategy is designed and applied over the resultant set of partitions in order to assess the closed-loop performance. Results obtained when used several simulation scenarios show the effectiveness of both the partitioning approach and the DMPC strategy in terms of the reduced computational burden and, at the same time, of the admissible loss of performance in contrast to a centralised MPC strategy.     

Self-adaptive and reconfigurable distributed computing systems

In recent time, the applications of biologically-inspired computing models into various domains of computing fields have gained attention due to a set of advantages. The bio-inspired distributed computing paradigm offers benefits such as, self-detection and self-reconfiguration capabilities of the computing systems. The large scale distributed systems suffer from the arbitrary failure of nodes and dynamic formation of network partitions at any point of time. This paper proposes a novel membrane algorithm for self-detection and self-reconfiguration of large distributed systems on the event of arbitrary node failures resulting in network partitioning. The algorithm is distributed in nature and, it is designed based on the hybridization of biological membrane computing model and cell-signaling mechanisms of biological cells. This paper presents the problem definition, design and analysis of the algorithm. The performance of the algorithm is evaluated through simulation. A detailed comparative analysis of the algorithm with respect to the other contemporary algorithms is presented.     

具有初始信息素的蚂蚁寻优软硬件划分算法

面向基于平台的系统芯片设计,提出具有初始信息素的蚂蚁寻优软硬件划分算法AOwIP.基本思想是：①利用基于平台的设计方法中已有参考设计的软硬件划分结果作为初始划分解,进行适当变换后生成初始信息素分布.②在所生成初始信息素分布的基础上,利用蚂蚁算法正反馈、高效收敛的优势寻求最优划分解.该算法利用基于平台的设计方法强调系统重用的优势,克服蚂蚁算法在求解软硬件划分问题时缺乏初始信息素的不足.实验表明,AOwIP算法有效提高了蚂蚁算法的最优解搜索效率.     

并行网络蠕虫模拟中任务优化划分的研究

为提高并行网络蠕虫模拟的性能，需要对蠕虫模拟任务进行合理的划分．鉴于基于图划分工具的任务划分方法存在的不足，提出了并行网络蠕虫模拟任务的优化划分方法：以并行网络蠕虫模拟运行时间估计模型作为优化目标函数，采用改进的模拟退火算法实现对蠕虫模拟任务的划分．在PDNS上进行的Slammer蠕虫传播模拟实验表明，该优化划分方法较基于图划分工具的方法提高模拟性能20％以上．     

异构平台多流编程机制的性能模型研究

多流编程机制为异构众核加速器提供流水、资源划分等多种资源使用方式,但如何选择有效使用方式目前缺乏指导。基于异构众核处理器Intel MIC上的hStreams,提出了针对单应用多流程序多硬件分区执行的性能模型,分析不同配置下多流程序性能差异的原因,指出了影响多流程序性能的关键因素,提出多流程序划分优化策略,同时所提性能模型能够帮助判断算法实现的效果。实验结果表明,性能模型与多流配置实际测试结果误差小于1%,根据性能模型指导调优稠密矩阵乘的多流程序,比单流程序获得了5.83%的性能提升。     

搜索空间平滑技术在软硬件划分中的应用

软硬件划分问题是软硬件协同设计的重要问题之一,它涉及到系统建模,划分算法和划分方案评价等问题,其中划分算法设计是关键点。以提高系统时间性能为目标,利用任务流图构造系统模型,在其上实现了基于优先权的评价函数,提出了搜索空间平滑技术与离散粒子群算法相结合的软硬件划分算法,并且解决了两者的融合问题,并能根据系统信息动态适应调整算法参数。实验结果表明,算法时间开销稳定,求解质量较高。     

基于遗传算法的软硬件划分方法

针对SOC软硬件协同设计中的软硬件划分问题,首先构建了软硬件划分的系统模型,讨论了影响SOC性能的价格、执行时间、功耗、面积等因素及其相互关系。在此基础上,提出了一个旨在实现最优性价比的目标函数,并结合软硬件划分问题自身的特点对传统遗传算法的遗传操作进行了改进,在Matlab中进行了软硬件划分方法的仿真,仿真实验获得的结果有力地证明了算法的稳定性和有效性。同时,该算法在MPEG-2视频解码芯片设计中得到了实际应用,芯片设计的结果良好地反映了设计目标,证明了算法的实用性。     

多级别嵌入式系统硬件/软件划分方法

对嵌入式系统应用中硬件/软件划分方法的研究是基于源代码级别或者二进制级别,可以利用在两个级别上的各自特点分别进行求解,利用了源代码级别的高级信息和二进制级别上性能准确估计的特点,先进行功能划分再进行二进制性能估算根据估计得到性能来动态划分应用。文章基于Petir网给出了系统划分模型,使用执行时间最少的增益函数启发下一步动作。算法可以用较少的硬件面积,提高执行时间。     

基于Hopfield神经网络和禁忌搜索的软/硬件功耗划分

在目前全球倡导“低碳经济”的背景下,随着嵌入式系统大量而广泛的使用,嵌入式软件功耗已成为嵌入式系统设计的一个关键因素,而软/硬件划分是嵌入式软件功耗优化的一种重要方法。首先在性能约束条件下,建立以嵌入式软件功耗为目标的软/硬件双路划分模型;然后,提出了一种基于离散Hopfield神经网络(HNN)和禁忌搜索(TS)融合的求解算法,采用离散Hopfield算法作为主算法能较快地获得可行解,使用禁忌搜索算法“禁忌”当前解而转移到目标函数的其他极小点,从而可跳出局部最优解而快速趋于全局最优解;最后,仿真实验表明,与同类算法相比,该算法不但具有搜索速度上的优势,而且求得全局最优解的概率更高。     

一种基于层次平台SoC设计中的软硬件划分方法

软硬件划分是SoC设计中的一个关键问题,合理的划分结果对最终生成的芯片在成本、性能、可扩展性等方面有重要影响。提出了在基于层次平台的SoC设计中,采用遗传算法进行软硬件划分的方法,并通过实验验证了其在SoC设计中的可行性。     

嵌入式系统软硬件自动划分方法研究

软硬件划分一直是嵌入式系统软硬件协同设计中的难点,如果离开具体系统,单纯的软硬件划分,其性能很难评估。本文提出基于系统体系结构,应用遗传算法来进行多目标优化的软硬件自动划分方法。在具体设计中,使用数据流图对系统建模,采用邻接表进行个体编码,定义交叉、变异操作,同时引入小生境技术,保持解的多样性。该方法为嵌入式系统软硬件自动划分提供一种新思路。     

Three level partitioning algorithm for circuit simulation on multiprocessor

Circuit partitioning issues for circuit simulation on distributed multiprocessors are discussed in this paper. An efficient three-level partitioning algorithm for large-scale circuit is proposed. Using this algorithm, we can partition a large-scale circuit into r subcircuits of similar size while keeping the interconnect set of nodes to a minimum. This algorithm can be implemented for parallel processing. Some examples are given to show the performance of the algorithm.     

Three-mode partitioning

The three-mode partitioning model is a clustering model for three-way three-mode data sets that implies a simultaneous partitioning of all three modes involved in the data. In the associated data analysis, a data array is approximated by a model array that can be represented by a three-mode partitioning model of a prespecified rank, minimizing a least squares loss function in terms of differences between data and model. Algorithms have been proposed for this minimization, but their performance is not yet clear. A framework for alternating least-squares methods is described in order to offset the performance problem. Furthermore, a number of both existing and novel algorithms are discussed within this framework. An extensive simulation study is reported in which these algorithms are evaluated and compared according to sensitivity to local optima. The recovery of the truth underlying the data is investigated in order to assess the optimal estimates. The ordering of the algorithms with respect to performance in finding the optimal solution appears to change as compared to the results obtained from the simulation study when a collection of four empirical data sets have been used. This finding is attributed to violations of the implicit stochastic model underlying both the least-squares loss function and the simulation study. Support for the latter attribution is found in a second simulation study.     

Benchmarking parallel processing platforms: an applicationsperspective

Given the increased availability of general purpose parallel computers two issues arise: One needs to compare the performance of the different available platforms using realistic examples, and it is necessary to write application software that can be ported easily in order to take advantage of different platforms. The authors address these issues from an applications point of view. They are interested in the use of general purpose parallel computers for simulation tasks needed during the design of very large scale integrated (VLSI) circuits. They characterize the simulation task as a useful benchmark and introduce a high level process view of parallel simulation that is helpful for deriving portable parallel programs. Details of the partitioning strategy and the simulation algorithm used in the application are given. They discuss their implementation on different parallel machines and give statistics of various experiments     

网格资源超图划分聚类任务调度

网格计算是当前一个活跃的研究领域,其中任务调度是实现网格计算目标的一个重要部分.为获得良好的网格任务调度性能,提出了一种基于资源超图划分聚类的网格任务调度算法RHPC.该算法根据网格环境下资源数量庞大、异构、多样的特点,在构建的网格资源超图模型基础上,预先对资源进行性能划分聚类,将任务与聚类资源相匹配并实施调度.模拟实验结果证明算法缩短了任务资源相匹配的时间,提高了任务调度的性能,是一种有效的网格任务调度算法.     

Identification of Neurofuzzy Models Using GTLS Parameter Estimation

In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation–maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.