View in EDS

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.

Saved in:
Bibliographic Details
Title: Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.
Authors: Laguna, Ignacio1 (AUTHOR) lagunaperalt1@llnl.gov, Tran, Anh2 (AUTHOR), Gopalakrishnan, Ganesh2 (AUTHOR)
Source: Parallel Computing. Sep2023, Vol. 117, pN.PAG-N.PAG. 1p.
Subjects: Heterogeneous computing, Information resources
Abstract: Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application's source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. We implement our approach in the Xscope framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. Xscope is able to identify inputs that trigger exceptions in about 73% of the tested functions. [ABSTRACT FROM AUTHOR]
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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 171587174
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Laguna%2C+Ignacio%22">Laguna, Ignacio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lagunaperalt1@llnl.gov</i><br /><searchLink fieldCode="AR" term="%22Tran%2C+Anh%22">Tran, Anh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gopalakrishnan%2C+Ganesh%22">Gopalakrishnan, Ganesh</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Parallel+Computing%22">Parallel Computing</searchLink>. Sep2023, Vol. 117, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Heterogeneous+computing%22">Heterogeneous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Information+resources%22">Information resources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application's source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. We implement our approach in the Xscope framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. Xscope is able to identify inputs that trigger exceptions in about 73% of the tested functions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=171587174
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.parco.2023.103042
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Heterogeneous computing
        Type: general
      – SubjectFull: Information resources
        Type: general
    Titles:
      – TitleFull: Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Laguna, Ignacio
      – PersonEntity:
          Name:
            NameFull: Tran, Anh
      – PersonEntity:
          Name:
            NameFull: Gopalakrishnan, Ganesh
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 01678191
          Numbering:
            – Type: volume
              Value: 117
          Titles:
            – TitleFull: Parallel Computing
              Type: main
ResultId 1