GAiN: Distributed Array Computation with Python

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Scientific computing makes use of very large, multidimensional numerical arrays - typically, gigabytes to terabytes in size - much larger than can fit on even the largest single compute node. Such arrays must be distributed across a "cluster" of nodes. Global Arrays is a cluster-based software system from Battelle Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate these arrays. Written in and for the C and FORTRAN programming languages, it takes advantage of high-performance cluster interconnections to allow any node in the cluster to access data on any other node very rapidly. ... continued below

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Daily, Jeffrey A. April 24, 2009.

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Scientific computing makes use of very large, multidimensional numerical arrays - typically, gigabytes to terabytes in size - much larger than can fit on even the largest single compute node. Such arrays must be distributed across a "cluster" of nodes. Global Arrays is a cluster-based software system from Battelle Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate these arrays. Written in and for the C and FORTRAN programming languages, it takes advantage of high-performance cluster interconnections to allow any node in the cluster to access data on any other node very rapidly. The "numpy" module is the de facto standard for numerical calculation in the Python programming language, a language whose use is growing rapidly in the scientific and engineering communities. numpy provides a powerful N-dimensional array class as well as other scientific computing capabilities. However, like the majority of the core Python modules, numpy is inherently serial. Our system, GAiN (Global Arrays in NumPy), is a parallel extension to Python that accesses Global Arrays through numpy. This allows parallel processing and/or larger problem sizes to be harnessed almost transparently within new or existing numpy programs.

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  • Report No.: PNNL-18355
  • Grant Number: AC05-76RL01830
  • DOI: 10.2172/1006323 | External Link
  • Office of Scientific & Technical Information Report Number: 1006323
  • Archival Resource Key: ark:/67531/metadc845132

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  • April 24, 2009

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  • May 19, 2016, 3:16 p.m.

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  • Nov. 28, 2016, 6:22 p.m.

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Daily, Jeffrey A. GAiN: Distributed Array Computation with Python, thesis or dissertation, April 24, 2009; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc845132/: accessed August 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.