Supporting data intensive applications with medium grained parallelism

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ADAMS is an ambitious effort to provide new database access paradigms for the kinds of scientific applications that require massively parallel access to very large data sets in order to be effective. Many of the Grand Challenge Problems fall into this category, as well as those kinds of scientific research which depend on widely distributed shared sets of disparate data. The essence of the ADAMS approach is to view data purely in functional terms, rather than the more traditional structural view in which multiple data items are aggregated into records or tuples of flat files. Further, ADAMS has been implemented ... continued below

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Pages: (25 p)

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Pfaltz, J.L.; French, J.C.; Grimshaw, A.S. & Son, S.H. April 1, 1992.

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Description

ADAMS is an ambitious effort to provide new database access paradigms for the kinds of scientific applications that require massively parallel access to very large data sets in order to be effective. Many of the Grand Challenge Problems fall into this category, as well as those kinds of scientific research which depend on widely distributed shared sets of disparate data. The essence of the ADAMS approach is to view data purely in functional terms, rather than the more traditional structural view in which multiple data items are aggregated into records or tuples of flat files. Further, ADAMS has been implemented as an embedded interface so that scientists can develop applications in the host programming language of their choice, often Fortran, Pascal, or C, and still access shared data generated in other environments. The syntax and semantics of ADAMS is essentially complete. The functional nature of the ADAMS data interface paradigm simplifies its implementation in a distributed environment, e.g., the Mentat run-time system, because one must only distribute functional servers, not pieces of data structures. However, this only opens up the possibility of effective parallel database processing; to realize this potential far more work must be done in the areas of data dependence, intra-statement parallelism, parallel query optimization, and maintaining consistency and reliability in concurrent systems. Discovering how to make effective parallel data access an actually in real scientific applications is the point of this research.

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Pages: (25 p)

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  • Other: DE92015101
  • Report No.: DOE/ER/25063-T13
  • Report No.: UVA--527435/CS92/101
  • Grant Number: FG05-88ER25063
  • DOI: 10.2172/5065639 | External Link
  • Office of Scientific & Technical Information Report Number: 5065639
  • Archival Resource Key: ark:/67531/metadc1054690

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • April 1, 1992

Added to The UNT Digital Library

  • Jan. 22, 2018, 7:23 a.m.

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  • Jan. 29, 2018, 1:53 p.m.

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Pfaltz, J.L.; French, J.C.; Grimshaw, A.S. & Son, S.H. Supporting data intensive applications with medium grained parallelism, report, April 1, 1992; United States. (digital.library.unt.edu/ark:/67531/metadc1054690/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.