View in EDS

EFFICIENT AND SCALABLE ALGORITHMS FOR SMOOTHED PARTICLE HYDRODYNAMICS ON HYBRID SHARED/DISTRIBUTED-MEMORY ARCHITECTURES.

Saved in:
Bibliographic Details
Title: EFFICIENT AND SCALABLE ALGORITHMS FOR SMOOTHED PARTICLE HYDRODYNAMICS ON HYBRID SHARED/DISTRIBUTED-MEMORY ARCHITECTURES.
Authors: GONNET, PEDRO1 gonnet@google.com
Source: SIAM Journal on Scientific Computing. 2015, Vol. 37 Issue 1, pC95-C121. 27p.
Subjects: Hydrodynamics, Hierarchical Bayes model, Hybrid computers (Computer architecture), Multiresolution time-domain method, Simulation methods & models
Abstract: This paper describes a new fast and implicitly parallel approach to neighbor-finding in multiresolution smoothed particle hydrodynamics (SPH) simulations. This new approach is based on hierarchical cell decompositions and sorted interactions, within a task-based formulation. It is shown to be faster than traditional tree-based codes and to scale better than domain decomposition-based approaches on hybrid shared/distributed-memory parallel architectures, e.g., clusters of multicores, achieving a 40x speedup over the Gadget-2 simulation code. [ABSTRACT FROM AUTHOR]
Copyright of SIAM Journal on Scientific Computing is the property of Society for Industrial & Applied Mathematics 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: 108605291
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: EFFICIENT AND SCALABLE ALGORITHMS FOR SMOOTHED PARTICLE HYDRODYNAMICS ON HYBRID SHARED/DISTRIBUTED-MEMORY ARCHITECTURES.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22GONNET%2C+PEDRO%22">GONNET, PEDRO</searchLink><relatesTo>1</relatesTo><i> gonnet@google.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22SIAM+Journal+on+Scientific+Computing%22">SIAM Journal on Scientific Computing</searchLink>. 2015, Vol. 37 Issue 1, pC95-C121. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Hydrodynamics%22">Hydrodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+Bayes+model%22">Hierarchical Bayes model</searchLink><br /><searchLink fieldCode="DE" term="%22Hybrid+computers+%28Computer+architecture%29%22">Hybrid computers (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Multiresolution+time-domain+method%22">Multiresolution time-domain method</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper describes a new fast and implicitly parallel approach to neighbor-finding in multiresolution smoothed particle hydrodynamics (SPH) simulations. This new approach is based on hierarchical cell decompositions and sorted interactions, within a task-based formulation. It is shown to be faster than traditional tree-based codes and to scale better than domain decomposition-based approaches on hybrid shared/distributed-memory parallel architectures, e.g., clusters of multicores, achieving a 40x speedup over the Gadget-2 simulation code. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of SIAM Journal on Scientific Computing is the property of Society for Industrial & Applied Mathematics 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=108605291
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1137/140964266
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: C95
    Subjects:
      – SubjectFull: Hydrodynamics
        Type: general
      – SubjectFull: Hierarchical Bayes model
        Type: general
      – SubjectFull: Hybrid computers (Computer architecture)
        Type: general
      – SubjectFull: Multiresolution time-domain method
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
    Titles:
      – TitleFull: EFFICIENT AND SCALABLE ALGORITHMS FOR SMOOTHED PARTICLE HYDRODYNAMICS ON HYBRID SHARED/DISTRIBUTED-MEMORY ARCHITECTURES.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: GONNET, PEDRO
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: 2015
              Type: published
              Y: 2015
          Identifiers:
            – Type: issn-print
              Value: 10648275
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: SIAM Journal on Scientific Computing
              Type: main
ResultId 1