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An Evaluation of Architectural Alternatives for Rapidly Growing Datasets, Active Disks, Clusters, SMPs

dc.contributor.authorUysal, Mustafaen_US
dc.contributor.authorAcharya, Anuragen_US
dc.contributor.authorSaltz, Joelen_US
dc.description.abstractGrowth and usage trends for several large datasets indicate that there is a need for architectures that scale the processing power as the dataset increases. In this paper, we evaluate three architectural alternatives for rapidly growing and frequently reprocessed datasets: active disks, clusters, and shared memory multiprocessors (SMPs). The focus of this evaluation is to identify potential bottlenecks in each of the alternative architectures and to determine the performance of these architectures for the applications of interest. We evaluate these architectural alternatives using a detailed simulator and a suite of nine applications. Our results indicate that for most of these applications Active Disk and cluster configurations were able to achieve significantly better performance than SMP configurations. Active Disk configurations were able to match (and in some cases improve upon) the performance of commodity cluster configurations. (Also cross-referenced as UMIACS-TR-98-68)en_US
dc.format.extent492475 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3957en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-98-68en_US
dc.titleAn Evaluation of Architectural Alternatives for Rapidly Growing Datasets, Active Disks, Clusters, SMPsen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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