Academic Journal

Auto-tuning full applications: A case study.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Auto-tuning full applications: A case study.
Συγγραφείς: Tiwari, Ananta, Hollingsworth, Jeffrey K, Chun Chen, Hall, Mary, Chunhua Liao, Quinlan, Daniel J, Chame, Jacqueline
Πηγή: International Journal of High Performance Computing Applications; Aug2011, Vol. 25 Issue 3, p286-294, 9p
Θεματικοί όροι: Computer programmers, Compilers (Computer programs), Kernel functions, Equipment & supplies
Περίληψη: In this paper, we take a concrete step towards materializing our long-term goal of providing a fully automatic end-to-end tuning infrastructure for arbitrary program components and full applications. We describe a general-purpose offline auto-tuning framework and apply it to an application benchmark, SMG2000, a semi-coarsening multigrid on structured grids. We show that the proposed system first extracts computationally intensive loop nests into separate executable functions, a code transformation called outlining. The outlined loop nests are then tuned by the framework and subsequently integrated back into the application. Each loop nest is optimized through a series of composable code transformations, with the transformations parameterized by unbound optimization parameters that are bound during the tuning process. The values for these parameters are selected using a search-based auto-tuner, which performs a parallel heuristic search for the best-performing optimized variants of the outlined loop nests. We show that our system pinpoints a code variant that performs 2.37 times faster than the original loop nest. When the full application is run using the code variant found by the system, the application’s performance improves by 27%. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. 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.)
Βάση Δεδομένων: Complementary Index
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  Data: Auto-tuning full applications: A case study.
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  Data: International Journal of High Performance Computing Applications; Aug2011, Vol. 25 Issue 3, p286-294, 9p
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  Data: <searchLink fieldCode="DE" term="%22Computer+programmers%22">Computer programmers</searchLink><br /><searchLink fieldCode="DE" term="%22Compilers+%28Computer+programs%29%22">Compilers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Equipment+%26+supplies%22">Equipment & supplies</searchLink>
– Name: Abstract
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  Group: Ab
  Data: In this paper, we take a concrete step towards materializing our long-term goal of providing a fully automatic end-to-end tuning infrastructure for arbitrary program components and full applications. We describe a general-purpose offline auto-tuning framework and apply it to an application benchmark, SMG2000, a semi-coarsening multigrid on structured grids. We show that the proposed system first extracts computationally intensive loop nests into separate executable functions, a code transformation called outlining. The outlined loop nests are then tuned by the framework and subsequently integrated back into the application. Each loop nest is optimized through a series of composable code transformations, with the transformations parameterized by unbound optimization parameters that are bound during the tuning process. The values for these parameters are selected using a search-based auto-tuner, which performs a parallel heuristic search for the best-performing optimized variants of the outlined loop nests. We show that our system pinpoints a code variant that performs 2.37 times faster than the original loop nest. When the full application is run using the code variant found by the system, the application’s performance improves by 27%. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. 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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        Value: 10.1177/1094342011414744
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        Text: English
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      – SubjectFull: Kernel functions
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              Text: Aug2011
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