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dc.contributor.authorBeck, Micahen_US
dc.contributor.authorBitton, Dinaen_US
dc.contributor.authorWilkinson, W. Kevinen_US
dc.date.accessioned2007-04-23T17:13:33Z
dc.date.available2007-04-23T17:13:33Z
dc.date.issued1986-03en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR86-741en_US
dc.identifier.urihttps://hdl.handle.net/1813/6581
dc.description.abstractA fundamental measure of processing power in a database management system is the performance of the sort utility it provides. When sorting a large data file on a serial computer, performance is limited by factors involving processor speed, memory capacity and I/O bandwidth. In this paper, we investigate the feasibility and efficiency of a parallel sort-merge algorithm through implementation on the JASMIN prototype, a backend multiprocessor built around a fast packet bus. We describe the design and implementation of a parallel sort utility that may become a building block for query processing in a database system that runs on JASMIN. We present and analyze the results of measurements corresponding to a range of file sizes and processor configurations. Our results show that using current, off-the-shelf technology coupled with a streamlined distributed operating system, three and five microprocessor configurations provide a very cost-effective sort of large files. The three processor configuration sorts a 100 megabyte file in one hour, which compares well with commercial sort packages available on high-performance mainframes. In additional experiments, we investigate a model to tune our sort software, and scale our results to higher processor and network capabilities.en_US
dc.format.extent2290575 bytes
dc.format.extent1090315 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleSorting Large Files on a Backend Multiprocessoren_US
dc.typetechnical reporten_US


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