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On the parallelization of irregular and dynamic programs

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Title: On the parallelization of irregular and dynamic programs
Authors: Plata, Oscar oscar@ac.uma.es, Asenjo, Rafael1, Gutiérrez, Eladio1, Corbera, Francisco1, Navarro, Angeles1, Zapata, Emilio L.1
Source: Parallel Computing. Jun2005, Vol. 31 Issue 6, p544-562. 19p.
Subjects: Computer files, Computer software, Computer programmers, Computer programming
Abstract: Abstract: Current compilers show ineffective when optimizing complex applications, both analyzing dependences and exploiting data locality and extracting parallelism. Complex applications may be characterized as irregular and dynamic. Irregular applications arrange data as multidimensional arrays and memory is referenced through array indirections. Dynamic applications organize data as pointer-based structures (lists, trees,…) and memory is referenced through pointers. In this paper we discuss a methodology we designed to develop efficient parallelization techniques for irregular and dynamic applications, that proceeds in three stages: recognizing the complex program structure, data analysis and program parallelization based on code/data transformations. Two case examples are analyzed in detail in the context of this methodology: irregular reductions and shape analysis for dynamic data structures. [Copyright &y& Elsevier]
Copyright of Parallel Computing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 18091780
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="AR" term="%22Plata%2C+Oscar%22">Plata, Oscar</searchLink><i> oscar@ac.uma.es</i><br /><searchLink fieldCode="AR" term="%22Asenjo%2C+Rafael%22">Asenjo, Rafael</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gutiérrez%2C+Eladio%22">Gutiérrez, Eladio</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Corbera%2C+Francisco%22">Corbera, Francisco</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Navarro%2C+Angeles%22">Navarro, Angeles</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zapata%2C+Emilio+L%2E%22">Zapata, Emilio L.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Parallel+Computing%22">Parallel Computing</searchLink>. Jun2005, Vol. 31 Issue 6, p544-562. 19p.
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  Data: Abstract: Current compilers show ineffective when optimizing complex applications, both analyzing dependences and exploiting data locality and extracting parallelism. Complex applications may be characterized as irregular and dynamic. Irregular applications arrange data as multidimensional arrays and memory is referenced through array indirections. Dynamic applications organize data as pointer-based structures (lists, trees,…) and memory is referenced through pointers. In this paper we discuss a methodology we designed to develop efficient parallelization techniques for irregular and dynamic applications, that proceeds in three stages: recognizing the complex program structure, data analysis and program parallelization based on code/data transformations. Two case examples are analyzed in detail in the context of this methodology: irregular reductions and shape analysis for dynamic data structures. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Parallel Computing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2005
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