This paper presents and discusses the design and the development of a pattern recognition agent based on neural networks. This agent is part of an intelligent navigation system, providing it with the necessary vision ...
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This paper presents and discusses the design and the development of a pattern recognition agent based on neural networks. This agent is part of an intelligent navigation system, providing it with the necessary vision abilities so that it can be placed on a strange environment in order to explore and recognise its structures and specificities. Although similar, the properties of the recognised objects change through time and according to each specific environment. The flexibility required by such recognition process was implemented by several pattern recognition agents. Each agent is based on a neural network and can be trained on-line by a parallel training algorithm to allow an effective real time utilisation.
This paper presents the complexity analysis and empirical results of a distributed selection algorithm. The algorithm uses the statistical properties of the data file. The objective of the algorithm is to minimize the...
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This paper presents the complexity analysis and empirical results of a distributed selection algorithm. The algorithm uses the statistical properties of the data file. The objective of the algorithm is to minimize the number of communication messages required for the whole selection process. The algorithm is designed to select the y th smallest key from a very large file which is physically distributed over many sites (stations). The size of the file is so large that it is not feasible or efficient to transfer all data to a single node as no node has sufficient memory space for internal sorting. The selection work will be shared by all sites involved and the load balancing is also ensured by the algorithm. The complexity of the algorithm is O(log log N/P) for a network with P stations and a file with N records.
In this paper we present a system for visualizing volume data from remote supercomputers (Perm Web). We have developed both parallel volume rendering algorithms, and the World Wide Web software for accessing the data ...
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ISBN:
(纸本)0819424285
In this paper we present a system for visualizing volume data from remote supercomputers (Perm Web). We have developed both parallel volume rendering algorithms, and the World Wide Web software for accessing the data at the remote sites. The implementation uses Hypertext Markup Language (HTML), Java, and Common Gateway Interface (CGI) scripts to connect World Wide Web (WWW) servers/clients to our volume renderers. The front ends are interactive Java classes for specification of view, shading, and classification inputs. We present performance results, and implementation details for connections to our computing resources at the University of California Santa Cruz including a MasPar MP-2, SGI Reality Engine-REP, and SGI Challenge machines. We apply the system to the task of visualizing trabecular bone from finite element simulations. Fast volume rendering on remote compute servers through a web interface allows us to increase the accessibility of the results to more users. User interface issues, overviews of parallel algorithm developments, and overall system interfaces and protocols are presented.
Vector computers have been extensively used for years in matrix algebra to treat with large dense matrix problems. However, if matrices are sparse and we use special storage schemes for them, vectorization provides a ...
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Vector computers have been extensively used for years in matrix algebra to treat with large dense matrix problems. However, if matrices are sparse and we use special storage schemes for them, vectorization provides a poor performance due to the great amount of indirections in the code. An alternative option is the utilization of a multiprocessor (or a cluster of workstations);in this case, a data parallel programming model also fails because of the reason pointed out for vector computers. Therefore, the best choice is to parallelize the corresponding algorithms using message passing routines. In order to discuss these features, we will focus on solving sparse linear least squares problems, which appear in several scientific areas such as structural analysis, geodetic survey, molecular structure and many others. Experimental results are obtained for vector and parallel computer architectures.
In this paper we examine the use of a shared memory programming model to address the problem of portability of application codes between distributed memory and shared memory architectures. We do this with an extension...
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parallel machines are an important part of the scientific application developer's tool box and the processing demands placed on these machines are rapidly increasing. Many scientific applications tend to perform h...
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Rapid advances in High Performance computing (HPC) and the Internet are heralding a paradigm shift to network-based scientific software servers, libraries, repositories and problem solving environments. According to t...
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Rapid advances in High Performance computing (HPC) and the Internet are heralding a paradigm shift to network-based scientific software servers, libraries, repositories and problem solving environments. According to this new paradigm, vital pieces of software and information required for a computation are distributed across a network and need to be identified and `linked' together at run time;this implies a `net-centric' and collaborative scenario for scientific computing. This scenario requires the application to dynamically choose the best among several competing resources that can solve a given problem. For these systems to become ubiquitous, efficient mechanisms for collaboration and automatic inference of the abilities of multiple `compute servers' need to be established. In this paper, we demonstrate a methodology to facilitate collaborative scientific computing. Our idea comprises of (i) a concept of `reasonableness' to automatically generate exemplars for learning the mapping from problems to `servers' and (ii) a neuro-fuzzy technique developed earlier by the authors that conducts supervised classification on the exemplars generated. Our techniques work in an on-line manner and cater to mutually non-exclusive classes which are critical in the collaborative networked computing landscape.
A Legendre-spectral element method is developed for numerical solution of unsteady flow and heat transfer associated with a spherical particle. A semi implicit approach is used to approximate the time derivative while...
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A Legendre-spectral element method is developed for numerical solution of unsteady flow and heat transfer associated with a spherical particle. A semi implicit approach is used to approximate the time derivative while a combined spectral method is used to discretize the equation in space. Functional parallelism offered by the Galerkin spectral method is exploited by carrying out the computations via distributedcomputing on a network of four workstations. Assessment of parallel performance of the numerical model indicates a significant reduction in computing time;hence a large speed-up ratio. Comparisons of the drag and the Nusselt numbers against previously reported results for several test problems show a very good agreement.
With advances in processor and networking technologies, current distributed-memory machines can achieve hundreds of Giga Floating-Point Operations Per Second (GFLOPS) of performance. By using such machines, many appli...
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ISBN:
(纸本)0818682272
With advances in processor and networking technologies, current distributed-memory machines can achieve hundreds of Giga Floating-Point Operations Per Second (GFLOPS) of performance. By using such machines, many application problems having regularly structured computations have been successfully parallelized using the explicit message passing paradigm, However, it is difficult to parallelize vision problems having irregularly structured computations. parallel solutions to these problems are characterized by uneven distribution of symbolic features among the processors, unbalanced workload, and irregular interprocessor data dependency caused by the input image. It is therefore necessary to develop efficient algorithmic techniques to achieve large speed-ups. In this paper, we propose an algorithmic framework to design efficient and portable parallel algorithms for irregular vision problems on distributed-memory machines. Based on this algorithmic framework, we develop techniques for task scheduling, load balancing, and overlapping communication with computation.
This paper introduces an algorithm that can generate huge node data flow by compiling existing programs. The purpose of this algorithm is to improve the speed of parallel processing and utilize the large amount of exi...
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ISBN:
(纸本)0818678763
This paper introduces an algorithm that can generate huge node data flow by compiling existing programs. The purpose of this algorithm is to improve the speed of parallel processing and utilize the large amount of existing program resources. In addition, this idea of huge node data flow algorithm can also be used in distributed processing and multi-thread processing.
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