Parallel computation of multiple biological sequence comparisons

A parallel implementation of an efficient method for comparison of multiple DNA sequences is presented. The method is described in terms of a conceptual tree data structure for the sequences to be compared. The parallel algorithm shows efficient utilization of processors on an Encore Multimax computer in a sample comparison of 11 sequences totaling over 4000 bases. Timing data show the strong influence of computer system details on this parallel program. Also presented is a graphics program for displaying multiple sequence comparison output data. The display is capable of representing large volumes of multiple sequence comparison data in a single plot. The program has several additional features that allow closer examination of subsets of sequences. A display of matches from the sample comparison reflects the known structure of these sequences.     

Parallel processing of biological sequence comparison algorithms

Comparison of biological (DNA or protein) sequences provides insight into molecular structure, function, and homology, and is increasingly important as the available databases become larger and more numerous. One method of increasing the speed of the calculations is to perform them in parallel. We present the results of initial investigations using the Intel iPSC/1 hypercube and the Connection Machine (CM-I) for these comparisons. Since these machines have very different architectures, the issues and performance trade-offs discussed have a wide applicability for the parallel processing of biological sequence comparisons. This research was supported in part by the Office of Naval Research under contact No. N00014-86-K-0310 and by NIH Grant T15 LM07056 from the National Library of Medicine.     

Parallel computation of spherical parameterizations for mesh analysis

Mesh parameterization is central to a broad spectrum of applications. In this paper, we present a novel approach to spherical mesh parameterization based on an iterative quadratic solver that is efficiently parallelizable on modern massively parallel architectures. We present an extensive analysis of performance results on both GPU and multicore architectures. We introduce a number of heuristics that exploit various system characteristics of the underlying architectures to speed up the parallel realization of our algorithms. Furthermore, we demonstrate the applicability of our approach to real-time feature detection, mesh decomposition and similarity-based 3D object retrieval. Finally, we offer visual results and a demonstration video.     

Parallel multischeme computation

Numerical experiments on multischeme computation to solve ordinary differential equation initial-value problems have been performed on a multiprocessor computer. A computation network of the schemes schedules the multischeme computation in parallel.     

Parallel symbolic computation in ACE

We present an overview of the ACE system, a sound and complete parallel implementation of Prolog that exploits parallelism transparently (i.e., without any user intervention) from AI programs and symbolic applications coded in Prolog. ACE simultaneously exploits all the major forms of parallelism – Or-parallelism, Independent And-parallelism, and Dependent And-parallelism – found in Prolog programs. These three varieties of parallelism are discussed in detail, along with the problems encountered in their practical exploitation. Our solutions to these problems, incorporated in the ACE system, are presented. The ACE system has been implemented on Sequent Symmetry and Sun Sparc Multiprocessors; performance results from this implementation for several AI programs are presented, which confirm the effectiveness of the choices made. This revised version was published online in June 2006 with corrections to the Cover Date.     

电力系统在线安全分析并行分布式计算

描述了一种电力系统在线安全分析并行分布式计算新方法,分析了该算法的数学模型、运行场景、任务划分与映射的方式.对于大规模的电力系统预想事故的子任务,通过使用一个计算引擎来执行主-从编程模式的应用.采用Java的RMI或Proactive实现方式,主-从编程模式可以很好地聚集互联网上的广域计算资源.测试与性能分析结果表明,该方法能够满足实时性、动态性的大规模电力系统在线安全分析的应用需求,具有较好的加速比与执行时间.     

Parallel computation in linear discrete filtering

Friedland's algorithm for decoupling the bias estimate in the linear optimal filtering is extended to the decoupling of the randomly varying bias terms.     

Sequence complexity for biological sequence analysis

A new statistical model for DNA considers a sequence to be a mixture of regions with little structure and regions that are approximate repeats of other subsequences, i.e. instances of repeats do not need to match each other exactly. Both forward- and reverse-complementary repeats are allowed. The model has a small number of parameters which are fitted to the data. In general there are many explanations for a given sequence and how to compute the total probability of the data given the model is shown. Computer algorithms are described for these tasks. The model can be used to compute the information content of a sequence, either in total or base by base. This amounts to looking at sequences from a data-compression point of view and it is argued that this is a good way to tackle intelligent sequence analysis in general.     

Parallel computation of disease transforms

Distance transforms are an important computational tool for the processing of binary images. For ann ×n image, distance transforms can be computed in time \(\mathcal{O}\) (n) on a mesh-connected computer and in polylogarithmic time on hypercube related structures. We investigate the possibilities of computing distance transforms in polylogarithmic time on the pyramid computer and the mesh of trees. For the pyramid, we obtain a polynomial lower bound using a result by Miller and Stout, so we turn our attention to the mesh of trees. We give a very simple \(\mathcal{O}\) (logn) algorithm for the distance transform with respect to theL ₁-metric, an \(\mathcal{O}\) (log² n) algorithm for the transform with respect to theL _∞-metric, and find that the Euclidean metric is much more difficult. Based on evidence from number theory, we conjecture the impossibility of computing the Euclidean distance transform in polylogarithmic time on a mesh of trees. Instead, we approximate the distance transform up to a given error. This works for anyL _k -metric and takes time \(\mathcal{O}\) (log³ n).     

Parallel computation of contour properties

Some contour properties can be derived in parallel by a string or cycle of automata in linear time, faster than can be done with a single processor. In particular, the intersection points of two contours, the straightness of a line, the union or intersection of two contours, and polygonal approximations of a contour are computed in linear time.     

Parallel computation for natural convection

Parallel computation for two-dimensional convective flows in cavities with adiabatic horizontal boundaries and driven by differential heating of the two vertical end walls are investigated using the Intel Paragon, Intel Touchstone Delta, Cray T3D and IBM SP2. The numerical scheme, including a parallel multigrid solver, and domain decomposition techniques for parallel computing are discussed in detail. Performance comparisons are made for the different parallel systems, and numerical results using various numbers of processors are discussed. © 1997 John Wiley & Sons, Ltd.     

Parallel computation of disease transforms

Distance transforms are an important computational tool for the processing of binary images. For ann ×n image, distance transforms can be computed in time (n) on a mesh-connected computer and in polylogarithmic time on hypercube related structures. We investigate the possibilities of computing distance transforms in polylogarithmic time on the pyramid computer and the mesh of trees. For the pyramid, we obtain a polynomial lower bound using a result by Miller and Stout, so we turn our attention to the mesh of trees. We give a very simple (logn) algorithm for the distance transform with respect to theL ₁-metric, an (log² n) algorithm for the transform with respect to theL -metric, and find that the Euclidean metric is much more difficult. Based on evidence from number theory, we conjecture the impossibility of computing the Euclidean distance transform in polylogarithmic time on a mesh of trees. Instead, we approximate the distance transform up to a given error. This works for anyL _k -metric and takes time (log³ n).This research was supported by the Deutsche Forschungsgemeinschaft under Grant Al 253/1-1, Schwerpunktprogramm Datenstrukturen und effiziente Algorithmen.     

Parallel multischeme computation with the switch of computation history

A parallel multischeme computation in the solutions of differential equation initial-value problems has been studied. The mathematical switch of computation history is used successfully in the identification of the best approximation among all available ones at a computing step. A solution correction factor is also developed to achieve an extra four digits in solution accuracy. Based on our results, if the truncation error of computation history as we defined it can be properly utilized, then a computation engaging high-order schemes or using fine grids may be unnecessary.     

Parallel strategies for the local biological sequence alignment in a cluster of workstations

Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith–Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith–Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used.     

含先验信息的学习机在生物序列分析中的应用

生物序列分析是机器学习和数据挖掘技术一个重要的应用领域。它的特别之处在于,很多有领域背景的先验知识可以在分析过程中得到利用,从而改善分析的效果。在对蛋白质的乙酰化修饰的预测过程中,通过合理地利用先验信息,改进模式提取方法,能够显著地提高支持向量机模型的预测性能。     

Parallel skyline computation on multicore architectures

With the advent of multicore processors, it has become imperative to write parallel programs if one wishes to exploit the next generation of processors. This paper deals with skyline computation as a case study of parallelizing database operations on multicore architectures. First we parallelize three sequential skyline algorithms, BBS, SFS, and SSkyline, to see if the design principles of sequential skyline computation also extend to parallel skyline computation. Then we develop a new parallel skyline algorithm PSkyline based on the divide-and-conquer strategy. Experimental results show that all the algorithms successfully utilize multiple cores to achieve a reasonable speedup. In particular, PSkyline achieves a speedup approximately proportional to the number of cores when it needs a parallel computation the most.     

Parallel computation on multilayer cluster grids

Cluster architectures are increasingly used to solve high‐performance computing applications. To build more computational power, sets of clusters, interconnected by high‐speed networks, can be used in an elaboration to form a cluster grid. In this type of architecture, it is difficult to exploit all the internal resources of a cluster, because each one can be shielded by a firewall and is usually configured with machines where there is only one visible IP front‐end node that hides all its internal nodes from the external world. The exploitation of resources is even more complicated if we consider the general case where each internal node of a cluster can be a front‐end node of an another cluster. This type of architecture has been defined as a multilayer cluster grid. In this paper, a Parallel Virtual Machine (PVM) extension is presented which, through a middleware solution based on the H2O distributed metacomputing framework, permits the building of a parallel virtual machine in a multilayer cluster grid environment. In addition, the existing code written for PVM can be executed into this environment without modifications. Copyright © 2007 John Wiley & Sons, Ltd.     

Parallel computation of manipulator inverse dynamics

In this article, parallel computation of manipulator inverse dynamics is investigated. A hierarchical graph-based mapping approach is devised to analyze the inherent parallelism in the Newton-Euler formulation at several computational levels, and to derive the features of an abstract architecture for exploitation of parallelism. At each level, a parallel algorithm represents the application of a parallel model of computation that transforms the computation into a graph whose structure defines the features of an abstract architecture, i.e., number of processors, communication structure, etc. Data flow analysis is employed to derive the time lower bound in the computation as well as the sequencing of the abstract architecture. The features of the target architecture are defined by optimization of the abstract architecture to exploit maximum parallelism while minimizing various overheads and architectural complexity. An algorithmically specialized, highly parallel, MIMD-SIMD architecture is designed and implemented that is capable of efficient exploitation of parallelism at several computational levels. The computation time of the Newton-Euler formulation for a 6-degree-of-freedom (dof) general manipulator is measured as 187 μs. The increase in computation time for each additional dof is 23 μs, which leads to a computation time of less than 500 μs, even for a 12-dof redundant arm.