Periodical
Προβολή σε EDS

Stochastic Program Optimization.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Stochastic Program Optimization.
Συγγραφείς: Schkufza, Eric1 (AUTHOR) eschkufz@cs.stanford.edu, Sharma, Rahul1 (AUTHOR) sharmar@cs.stanford.edu, Aiken, Alex1 (AUTHOR) aiken@cs.stanford.edu
Πηγή: Communications of the ACM. Feb2016, Vol. 59 Issue 2, p114-122. 9p. 2 Diagrams, 5 Charts, 4 Graphs.
Θεματικοί όροι: *Optimizers (Computer software), *Stochastic processes, *Markov chain Monte Carlo, Compilers (Computer programs), Program transformation
Περίληψη: The optimization of short sequences of loop-free, fixed-point assembly code sequences is an important problem in high-performance computing. However, the competing constraints of transformation correctness and performance improvement often force even special purpose compilers to produce sub-optimal code. We show that by encoding these constraints as terms in a cost function, and using a Markov Chain Monte Carlo sampler to rapidly explore the space of all possible code sequences, we are able to generate aggressively optimized versions of a given target code sequence. Beginning from binaries compiled by llvm −O0, we are able to produce provably correct code sequences that either match or outperform the code produced by gcc −O3, icc −O3, and in some cases expert handwritten assembly. [ABSTRACT FROM AUTHOR]
Copyright of Communications of the ACM is the property of Association for Computing Machinery 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: Business Source Index
FullText Links:
  – Type: other
Text:
  Availability: 0
Header DbId: bsx
DbLabel: Business Source Index
An: 112736862
RelevancyScore: 1177
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 1177.00793457031
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Stochastic Program Optimization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Schkufza%2C+Eric%22">Schkufza, Eric</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> eschkufz@cs.stanford.edu</i><br /><searchLink fieldCode="AR" term="%22Sharma%2C+Rahul%22">Sharma, Rahul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sharmar@cs.stanford.edu</i><br /><searchLink fieldCode="AR" term="%22Aiken%2C+Alex%22">Aiken, Alex</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aiken@cs.stanford.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Communications+of+the+ACM%22">Communications of the ACM</searchLink>. Feb2016, Vol. 59 Issue 2, p114-122. 9p. 2 Diagrams, 5 Charts, 4 Graphs.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Optimizers+%28Computer+software%29%22">Optimizers (Computer software)</searchLink><br />*<searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Compilers+%28Computer+programs%29%22">Compilers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Program+transformation%22">Program transformation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The optimization of short sequences of loop-free, fixed-point assembly code sequences is an important problem in high-performance computing. However, the competing constraints of transformation correctness and performance improvement often force even special purpose compilers to produce sub-optimal code. We show that by encoding these constraints as terms in a cost function, and using a Markov Chain Monte Carlo sampler to rapidly explore the space of all possible code sequences, we are able to generate aggressively optimized versions of a given target code sequence. Beginning from binaries compiled by llvm −O0, we are able to produce provably correct code sequences that either match or outperform the code produced by gcc −O3, icc −O3, and in some cases expert handwritten assembly. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Communications of the ACM is the property of Association for Computing Machinery 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsx&AN=112736862
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1145/2863701
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 114
    Subjects:
      – SubjectFull: Optimizers (Computer software)
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
      – SubjectFull: Compilers (Computer programs)
        Type: general
      – SubjectFull: Program transformation
        Type: general
    Titles:
      – TitleFull: Stochastic Program Optimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Schkufza, Eric
      – PersonEntity:
          Name:
            NameFull: Sharma, Rahul
      – PersonEntity:
          Name:
            NameFull: Aiken, Alex
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2016
              Type: published
              Y: 2016
          Identifiers:
            – Type: issn-print
              Value: 00010782
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Communications of the ACM
              Type: main
ResultId 1