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Case study of multiple trace transform implementations.

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Title: Case study of multiple trace transform implementations.
Authors: Besard, Tim1, De Sutter, Bjorn1 Bjorn.DeSutter@elis.ugent.be, Frías-Velázquez, Andrés2, Philips, Wilfried2
Source: International Journal of High Performance Computing Applications. Nov2015, Vol. 29 Issue 4, p489-505. 17p.
Subjects: MatLab (Computer software), Julia (Computer program language), CUDA (Computer architecture), Image processing, NVIDIA Corp., Graphics processing units
Abstract: Scientific algorithms are designed and implemented in a variety of programming languages. Depending on the exact application, some languages are a better choice than others: some offer a productive environment while others focus on performance. Selecting a language is often difficult, with poor choices resulting in much higher development times.By implementing a case study algorithm in multiple programming languages, we compare their pros and cons. As a case study, we selected the trace transform, an image processing algorithm from the widely used class of integral transforms. We describe each implementation, including a highly optimized version for NVIDIA graphics processing units, and present a productivity overview and an in-depth performance analysis, from which we draw more generic conclusions.We have found that MATLAB is still the best choice overall, but Julia proves an interesting emerging choice. For realistic images, our compute unified device architecture (CUDA) implementation offers the best performance, albeit at a high development cost. [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.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+High+Performance+Computing+Applications%22">International Journal of High Performance Computing Applications</searchLink>. Nov2015, Vol. 29 Issue 4, p489-505. 17p.
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  Data: <searchLink fieldCode="DE" term="%22MatLab+%28Computer+software%29%22">MatLab (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Julia+%28Computer+program+language%29%22">Julia (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22CUDA+%28Computer+architecture%29%22">CUDA (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22NVIDIA+Corp%2E%22">NVIDIA Corp.</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink>
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  Data: Scientific algorithms are designed and implemented in a variety of programming languages. Depending on the exact application, some languages are a better choice than others: some offer a productive environment while others focus on performance. Selecting a language is often difficult, with poor choices resulting in much higher development times.By implementing a case study algorithm in multiple programming languages, we compare their pros and cons. As a case study, we selected the trace transform, an image processing algorithm from the widely used class of integral transforms. We describe each implementation, including a highly optimized version for NVIDIA graphics processing units, and present a productivity overview and an in-depth performance analysis, from which we draw more generic conclusions.We have found that MATLAB is still the best choice overall, but Julia proves an interesting emerging choice. For realistic images, our compute unified device architecture (CUDA) implementation offers the best performance, albeit at a high development cost. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  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: 10.1177/1094342015584091
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 489
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      – SubjectFull: MatLab (Computer software)
        Type: general
      – SubjectFull: Julia (Computer program language)
        Type: general
      – SubjectFull: CUDA (Computer architecture)
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: NVIDIA Corp.
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      – SubjectFull: Graphics processing units
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      – TitleFull: Case study of multiple trace transform implementations.
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            NameFull: Besard, Tim
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            – D: 01
              M: 11
              Text: Nov2015
              Type: published
              Y: 2015
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