138 research outputs found

    An open interface for parallelization of traffic simulation

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    In this paper, we present the implementation of a parallel road traffic simulation using the concept of Lane Cut Points (LCPs) in the Spider programming environment. LCPs are storage buffers inserted into lane data structures at the road network partition edges. Vehicles enter a partition at the edges from an LCP and exit a partition edge into an LCP at the end of every simulation step. Spider, a parallel programming environment, which runs on PVM, coordinates the execution of the parallel traffic simulation

    Message‐passing performance of various computers

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    Message-passing performance of various computers

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    An experience in building a parallel and distributed problem-solving environment

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    We describe our experimentation with the design and implementation of specific environments, consisting of heterogeneous computational, visualization, and control components. We illustrate the approach with the design of a problem-solving environment supporting the execution of genetic algorithms. We describe a prototype steering parallel execution, visualization, and steering. A life cycle for the development of applications based an genetic algorithms is proposed.publishersversionpublishe

    Cluster Computing in the Classroom: Topics, Guidelines, and Experiences

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    With the progress of research on cluster computing, more and more universities have begun to offer various courses covering cluster computing. A wide variety of content can be taught in these courses. Because of this, a difficulty that arises is the selection of appropriate course material. The selection is complicated by the fact that some content in cluster computing is also covered by other courses such as operating systems, networking, or computer architecture. In addition, the background of students enrolled in cluster computing courses varies. These aspects of cluster computing make the development of good course material difficult. Combining our experiences in teaching cluster computing in several universities in the USA and Australia and conducting tutorials at many international conferences all over the world, we present prospective topics in cluster computing along with a wide variety of information sources (books, software, and materials on the web) from which instructors can choose. The course material described includes system architecture, parallel programming, algorithms, and applications. Instructors are advised to choose selected units in each of the topical areas and develop their own syllabus to meet course objectives. For example, a full course can be taught on system architecture for core computer science students. Or, a course on parallel programming could contain a brief coverage of system architecture and then devote the majority of time to programming methods. Other combinations are also possible. We share our experiences in teaching cluster computing and the topics we have chosen depending on course objectives

    R/parallel – speeding up bioinformatics analysis with R

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    Background: R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians. Results: We have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of today's desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores. Conclusion: R/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.

    GRID Portal Application Visualization

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    Parameter studies are useful applications for researchers; however, these programs, although helpful, tend to be computationally expensive and due to their long execution time become tedious to execute. In this project we explored a method of implementing a parameter study module for the P-GRADE Portal at MTA-SZTAKI; Budapest, Hungary, an existing parallel application that allows users to create and execute a parallel program in an efficient manner without knowledge of MPI or PVM programming
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