Data Access Performance Through Parallelization and Vectored Access: Some Results

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High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency. Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their ... continued below

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9 pages

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Furano, Fabrizio; /INFN, Padua; Hanushevsky, Andrew & /SLAC November 10, 2011.

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High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency. Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their performance and characteristics. These techniques, if used together with multiple streams data access, can also be effective in allowing to efficiently and transparently deal with data repositories accessible via a Wide Area Network.

Physical Description

9 pages

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  • Journal Name: J.Phys.Conf.Ser.119:072016,2008; Conference: Prepared for International Conference on Computing in High Energy and Nuclear Physics (CHEP 07), Victoria, BC, Canada, 2-7 Sep 2007

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  • Report No.: SLAC-PUB-14708
  • Grant Number: AC02-76SF00515
  • Office of Scientific & Technical Information Report Number: 1029160
  • Archival Resource Key: ark:/67531/metadc834202

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  • November 10, 2011

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  • May 19, 2016, 3:16 p.m.

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  • Dec. 7, 2016, 10:25 a.m.

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Furano, Fabrizio; /INFN, Padua; Hanushevsky, Andrew & /SLAC. Data Access Performance Through Parallelization and Vectored Access: Some Results, article, November 10, 2011; United States. (digital.library.unt.edu/ark:/67531/metadc834202/: accessed June 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.