64,167 research outputs found

    Workload characterization of the shared/buy-in computing cluster at Boston University

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    Computing clusters provide a complete environment for computational research, including bio-informatics, machine learning, and image processing. The Shared Computing Cluster (SCC) at Boston University is based on a shared/buy-in architecture that combines shared computers, which are free to be used by all users, and buy-in computers, which are computers purchased by users for semi-exclusive use. Although there exists significant work on characterizing the performance of computing clusters, little is known about shared/buy-in architectures. Using data traces, we statistically analyze the performance of the SCC. Our results show that the average waiting time of a buy-in job is 16.1% shorter than that of a shared job. Furthermore, we identify parameters that have a major impact on the performance experienced by shared and buy-in jobs. These parameters include the type of parallel environment and the run time limit (i.e., the maximum time during which a job can use a resource). Finally, we show that the semi-exclusive paradigm, which allows any SCC user to use idle buy-in resources for a limited time, increases the utilization of buy-in resources by 17.4%, thus significantly improving the performance of the system as a whole.http://people.bu.edu/staro/MIT_Conference_Yoni.pdfAccepted manuscrip

    Investigating grid computing technologies for use with commercial simulation packages

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    As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster

    The NorduGrid architecture and tools

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    The NorduGrid project designed a Grid architecture with the primary goal to meet the requirements of production tasks of the LHC experiments. While it is meant to be a rather generic Grid system, it puts emphasis on batch processing suitable for problems encountered in High Energy Physics. The NorduGrid architecture implementation uses the \globus{} as the foundation for various components, developed by the project. While introducing new services, the NorduGrid does not modify the Globus tools, such that the two can eventually co-exist. The NorduGrid topology is decentralized, avoiding a single point of failure. The NorduGrid architecture is thus a light-weight, non-invasive and dynamic one, while robust and scalable, capable of meeting most challenging tasks of High Energy Physics.Comment: Talk from the 2003 Computing in High Energy Physics and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 9 pages,LaTeX, 4 figures. PSN MOAT00
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