动态故障树割序集分析的模块化方法

针对割序集模型较高的复杂度,提出静态子树模块化和动态子树模块化2种简化方法。利用模块化方法将动态故障树划分为多个静态子树和动态子树。对完全由静态门构成的静态子树采用二叉决策图计算其发生概率;对动态子树采用割序集模型进行分析,将其中包含的静态子树作为一个整体进行处理。通过实例阐述模块化方法的应用过程,算例分析结果表明,该方法能有效降低割序集模型的复杂度。     

Parallelizing the Data Cube

This paper presents a general methodology for the efficient parallelization of existing data cube construction algorithms. We describe two different partitioning strategies, one for top-down and one for bottom-up cube algorithms. Both partitioning strategies assign subcubes to individual processors in such a way that the loads assigned to the processors are balanced. Our methods reduce inter processor communication overhead by partitioning the load in advance instead of computing each individual group-by in parallel. Our partitioning strategies create a small number of coarse tasks. This allows for sharing of prefixes and sort orders between different group-by computations. Our methods enable code reuse by permitting the use of existing sequential (external memory) data cube algorithms for the subcube computations on each processor. This supports the transfer of optimized sequential data cube code to a parallel setting.The bottom-up partitioning strategy balances the number of single attribute external memory sorts made by each processor. The top-down strategy partitions a weighted tree in which weights reflect algorithm specific cost measures like estimated group-by sizes. Both partitioning approaches can be implemented on any shared disk type parallel machine composed of p processors connected via an interconnection fabric and with access to a shared parallel disk array.We have implemented our parallel top-down data cube construction method in C++ with the MPI message passing library for communication and the LEDA library for the required graph algorithms. We tested our code on an eight processor cluster, using a variety of different data sets with a range of sizes, dimensions, density, and skew. Comparison tests were performed on a SunFire 6800. The tests show that our partitioning strategies generate a close to optimal load balance between processors. The actual run times observed show an optimal speedup of p.     

Z树：一个高维度的数据索引结构

Z树能够高效地处理对高维度数据集的矩形区域查询和最邻近搜索。它按照节点的形状变化量优化数据的插入位置，使节点形状趋于合理。文章给出了一个新的无重叠分裂算法，减少超级节点的产生。引入了动态剪枝和重新插入策略，压缩超级节点的数量和体积。提出了矩形节点的球形化方法和最优子树搜索算法。实验表明Z树的矩形区域查询和最邻近搜索的效率远远高于X树和SR树。     

Parallel preconditioned WENO scheme for three-dimensional flow simulation of NREL Phase VI Rotor

The preconditioned weighted essentially non-oscillatory (P-WENO) solver for viscous flows (Huang et al. (2009) [9]) is extended to non-inertial reference frames. In the present scheme, patched multi-block grid system is employed and parallel computing is adopted as well. With the present parallel P-WENO solver, three-dimensional flows of the Phase VI Rotor from National Renewable Energy Laboratory (NREL) can be simulated and analyzed for different wind speeds. Our simulation results show good agreement with the numerical predictions based on incompressible Navier–Stokes (N–S) equations as well as the available wind tunnel data from NREL. The flow phenomena, including separation and attachment line, can be captured by the present scheme. The parallel strategy adopted is a block-domain decomposition method for the patched multi-block grid system. To balance the load among different computing nodes, a Tabu search algorithm is adopted for the parallelization. The parallel efficiency of the parallel P-WENO scheme is examined for node numbers ranging from 1 to 64. It is found that the parallel efficiency is monotonically decreased as the node number adopted is increased; the parallel efficiency is retained over 90% for all cases of different node numbers. Due to the high parallel efficiency, our parallel P-WENO solver is validated for applying to practical fluid problems from compressible to incompressible limits.     

Parallel computing techniques applied to the simultaneous design of structure and material

In this work a computational procedure for two-scale topology optimization problem using parallel computing techniques is developed. The goal is to obtain simultaneously the best structure and material, minimizing structural compliance. An algorithmic strategy is presented in a suitable way for parallelization. In terms of parallel computing facilities, an IBM Cluster 1350 is used comprising 70 computing nodes each with two dual core processors, for a total of 280 cores. Scalability studies are performed with mechanical structures of low/moderate dimensions. Finally the applicability of the proposed methodology is demonstrated solving a grand challenge problem that is the simulation of trabecular bone adaptation.     

Embedded Parallelization Approach for Optimization in Aerodynamic Design

A new efficient parallelization strategy for optimization of aerodynamic shapes is proposed. The optimization method employs a full Navier-Stokes solver for accurate estimation of the objective function. As such it requires huge computational resources which makes efficient parallelization crucial for successful promotion of the method to an engineering environment. The algorithm is based on a multilevel embedded parallelization approach, which includes (1) parallelization of the multiblock full Navier-Stokes solver with parallel CFD evaluation of objective function, (2) parallelization of optimization process with parallel optimal search on multiple search domains and, finally, (3) parallel grid generation. Applications (implemented on a 144-processors distributed memory cluster) include various transonic profile optimizations in the presence of nonlinear constraints. The results demonstrate that the approach combines high accuracy of optimization with high parallel efficiency. The proposed multilevel parallelization which efficiently makes use of computational power supplied by multiprocessor systems, leads to a significant computational time-saving and allows application of the method to practical aerodynamic design in the aircraft industry.     

Nagging: A scalable fault-tolerant paradigm for distributed search

This paper describes nagging, a technique for parallelizing search in a heterogeneous distributed computing environment. Nagging exploits the speedup anomaly often observed when parallelizing problems by playing multiple reformulations of the problem or portions of the problem against each other. Nagging is both fault tolerant and robust to long message latencies. In this paper, we show how nagging can be used to parallelize several different algorithms drawn from the artificial intelligence literature, and describe how nagging can be combined with partitioning, the more traditional search parallelization strategy. We present a theoretical analysis of the advantage of nagging with respect to partitioning, and give empirical results obtained on a cluster of 64 processors that demonstrate nagging's effectiveness and scalability as applied to A^* search, β minimax game tree search, and the Davis–Putnam algorithm.     

基于静态前提的谓词知识树分解策略

智能规划问题实质是一种搜索问题,通常需采用某种策略来缩小搜索空间,提高规划效率。在“以谓词为主体”的规划求解方法中,规划树的生成效率将直接影响规划求解效率。为此,提出了基于静态前提的谓词知识树分解策略,并给出了相应的分解算法。对任意一个规划领域,利用该分解算法可将知识树分解成若干个较小规模的知识子树。在规划求解的过程中,利用知识子树可有效地减少搜索空间,从而快速生成规划树,提高规划效率。同时,利用知识子树还可提取出隐含在动作描述中的领域知识。实验结果表明该分解算法是有效的。     

Hybrid dynamic iterations for the solution of initial value problems

Many scientific problems are posed as Ordinary Differential Equations (ODEs). A large subset of these are initial value problems, which are typically solved numerically. The solution starts by using a known state space of the ODE system to determine the state at a subsequent point in time. This process is repeated several times. When the computational demand is high due to large state space, parallel computers can be used efficiently to reduce the time to solution. Conventional parallelization strategies distribute the state space of the problem amongst cores and distribute the task of computing for a single time step amongst the cores. They are not effective when the computational problems have fine granularity, for example, when the state space is relatively small and the computational effort arises largely from the long time span of the initial value problem. We propose a hybrid dynamic iterations method¹ which combines conventional sequential ODE solvers with dynamic iterations to parallelize the time domain. Empirical results demonstrate a factor of two to four improvement in performance of the hybrid dynamic iterations method over a conventional ODE solver on an 8 core processor. Compared to Picard iterations (also parallelized in the time domain), the proposed method shows better convergence and speedup results when high accuracy is required.     

Shared-nothing并行数据库系统查询优化技术

查询优化是并行数据库系统的核心技术。该文介绍作者自行研制的一个Ｓｈａｒｅｄ－ｎｏｔｈｉｎｇ并行数据库系统ＰＢＡＳＥ／２中独特的两阶段优化策略。为了缩减并行相称优化庞大的搜索空间,ＰＢＡＳＥ／２将并行查询优化划分为顺序优化和并行化两个在阶段。在顺序优化阶段对并行化后的通信代价进行预先估算,将通信开销加入顺序优化的代价模型,同时对动态规划搜索算法进行了修正和扩展,保证了顺序优化阶段得到的最小代价计划在     

一种并行XML数据库分片策略

主要研究XML文档的并行数据分片策略,以便能够并行处理XML查询.为了描述XML数据分片,提出了媒介节点的概念.一组媒介节点的集合可以将一棵XML数据树分割成一棵根树和一组子树的集合:根树将在所有站点中复制;而子树集合则可以根据用户查询的工作负载被均匀地分片到各个站点中.对于同一棵XML数据树,会有很多种媒介节点的集合;而不同的媒介节点集合会产生不同的数据分片结果.然后,依据各个数据分片中的用户查询工作量是否均衡,来衡量一个分片的好坏.选择一组最佳的媒介节点集合是一个NP-hard问题.为了解决此问题,设计了一组启发式优化规则.基于这一思想,提出并实现了一种基于媒介节点的XML数据分片算法WIN(workload-aware intermediary nodes data placement strategy).大量实验结果证明:WIN算法的性能要优于以往的并行XML数据分片策略.     

Parallel genetic algorithms: a survey and problem state of the art

In relation with development of computer capabilities and the appearance of multicore processors, parallel computing made it possible to reduce the time for solution of optimization problems. At present of interest are methods of parallel computing for genetic algorithms using the evolutionary model of development in which the main component is the population of species (set of alternative solutions to the problem). In this case, the algorithm efficiency increases due to parallel development of several populations. The survey of basic parallelization strategies and the most interesting models of their implementation are presented. Theoretical ideas on improvement of existing parallelization mechanisms for genetic algorithms are described. A modified model of parallel genetic algorithm is developed. Since genetic algorithms are used for solution of optimization problems, the proposed model was studied for the problem of optimization of a multicriteria function. The algorithm capabilities of getting out of local optima and the influence of algorithm parameters on the deep extremum search dynamics were studied. The conclusion on efficiency of application of dynamic connections of processes, rather than static connections, is made. New mechanisms for implementation and analysis of efficiency of dynamic connections for distributed computing in genetic algorithms are necessary.     

On the Performance of Randomized Embedding of Reproduction Trees in Static Networks

High performance computing requires high quality load distribution of processes of a parallel application over processors in a parallel computer at runtime such that both the maximum load and dilation are minimized. The performance of a simple randomized tree embedding algorithm that dynamically supports tree-structured parallel computations on arbitrary static networks is analyzed in this paper. The algorithm spreads newly created tree nodes to neighboring processors, which actually provides randomized dilation-1 tree embedding in static networks. We develop a linear system of equations that characterizes expected loads on all processors under the reproduction tree model, which can generate trees of arbitrary size and shape. It is shown that as the tree size becomes large, the asymptotic performance ratio of the randomized tree embedding algorithm is the ratio of the maximum processor degree to the average processor degree. This implies that the simple randomized tree embedding algorithm is able to generate high quality load distributions on virtually all static networks commonly employed in parallel and distributed computing.     

非规则数据场并行体绘制算法

并行算法是实现体绘制加速的重要途径，然而现有的并行体制绘制算法大部分是针对规则数据场的。     

A scalable,parallel algorithm for maximal clique enumeration

The problem of maximal clique enumeration (MCE) is to enumerate all of the maximal cliques in a graph. Once enumerated, maximal cliques are widely used to solve problems in areas such as 3-D protein structure alignment, genome mapping, gene expression analysis, and detection of social hierarchies. Even the most efficient serial MCE algorithms require large amounts of time to enumerate the maximal cliques in networks arising from these problems that contain hundreds, thousands, or larger numbers of vertices. The previous attempts to provide practical solutions to the MCE problem through parallel implementation have had limited success, largely due to a number of challenges inherent to the nature of the MCE combinatorial search space. On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times. On the other hand, the data-intensive nature of the MCE problem often makes naive dynamic load distribution strategies that require extensive data movement prohibitively expensive. As a result, good scaling of the overall execution time of parallel MCE algorithms has been reported for only up to a couple hundred processors. In this paper, we propose a parallel, scalable, and memory-efficient MCE algorithm for distributed and/or shared memory high performance computing architectures, whose runtime scales linearly for thousands of processors on real-world application graphs with hundreds and thousands of nodes. Its scalability and efficiency are attributed to the proposed: (a) representation of the search tree decomposition to enable parallelization; (b) parallel depth-first backtracking search to both constrain the search space and minimize memory requirement; (c) least stringent synchronization to minimize data movement; and (d) on-demand work stealing intelligently coupled with work stack splitting to minimize computing elements’ idle time. To the best of our knowledge, the proposed parallel MCE algorithm is the first to achieve a linear scaling runtime using up to 2048 processors on Cray XT machines for a number of real-world biological networks.     

游戏网格服务划分算法研究*

为了充分利用游戏网格的计算资源,使用其强大的并行计算能力,部署在游戏网格的网络游戏必须要划分成可以并行的多个服务。提出了一种基于动态二叉树的游戏网格服务划分算法;讨论了如何采用二叉树的数据结构来组织服务节点并根据服务节点的负载动态调整其服务划分;最后实现一个模拟游戏网格环境,通过实验结果证明该算法可以取得良好的性能。     

基于PRAM并行模型最大值查找的方法与改进

随着多处理器的出现,并行技术受到了广泛的关注,成为了加速处理问题速度的重要技术.但是使用并行技术在加速计算的同时也带来了对处理器数量需求的急剧提升,并行成本的显著增加.针对这一问题,通过研究基于PRAM （Parallel Random Access Machine）下的3种最大值查找并行算法中的不足,提出了一种比平衡树算法,快速查找法,双对数深度树方法并行成本（cost）更优的基于数据划分方法的最大值查找并行算法.基于数据划分方法的最大值查找算法有效的解决了现有并行方法中处理器工作量分配不均,对处理器需求过大,实现条件苛刻等问题.为此后类似并行算法降低并行成本提供一个方向.     

一种动态建树的方法

研究了绘制树状结构面临的难点问题,提出了一种动态构造树状结构的方法,设计了插入、删除节点等操作方法.在此基础上提出了一种高效的画树算法.与其它算法相比,该算法利用节点及子树边界的含义,通过不断调整移动子树来计算节点位置,进而实现在一个较小的区域内画树.该方法可以实时修改树的逻辑结构,并动态计算出节点位置,使得绘出的树即真实的树状结构.最后对该方法进行了时间和空间复杂度分析,对其应用前景进行了展望.