Scientific data analysis on data-parallel platforms.

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As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData ... continued below

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58 p.

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Ulmer, Craig D.; Bayer, Gregory W.; Choe, Yung Ryn & Roe, Diana C. September 1, 2010.

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Description

As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData dbX database, a LexisNexis DAS, and multiple Hadoop MapReduce clusters. The main contribution of this work is insight into the differences between these DWAs from a user's perspective. In addition, we present performance measurements for ten DWA systems to help understand the impact of different architectural trade-offs in these systems.

Physical Description

58 p.

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  • Report No.: SAND2010-7471
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/1011199 | External Link
  • Office of Scientific & Technical Information Report Number: 1011199
  • Archival Resource Key: ark:/67531/metadc845207

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Office of Scientific & Technical Information Technical Reports

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  • September 1, 2010

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Dec. 1, 2016, 6:49 p.m.

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Ulmer, Craig D.; Bayer, Gregory W.; Choe, Yung Ryn & Roe, Diana C. Scientific data analysis on data-parallel platforms., report, September 1, 2010; United States. (digital.library.unt.edu/ark:/67531/metadc845207/: accessed May 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.