Gast ::
| Hauptseite > Publikationsdatenbank > Using heterogeneous GPU nodes with a Cabana-based implementation of MPCD > print |
| Hauptseite > Publikationsdatenbank > Using heterogeneous GPU nodes with a Cabana-based implementation of MPCD > print |
| 001 | 1008596 | ||
| 005 | 20230929112536.0 | ||
| 024 | 7 | _ | |a 10.1016/j.parco.2023.103033 |2 doi |
| 024 | 7 | _ | |a 0167-8191 |2 ISSN |
| 024 | 7 | _ | |a 1872-7336 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-02429 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001031078800001 |2 WOS |
| 037 | _ | _ | |a FZJ-2023-02429 |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Halver, Rene |0 P:(DE-Juel1)132124 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Using heterogeneous GPU nodes with a Cabana-based implementation of MPCD |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2023 |b North-Holland, Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1689072216_32091 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a The Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. Cabana allows for a function portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system as well as analysis of said interplay with performance analysis tools. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to which heterogeneous applications might be more performant, using both CPU and GPU concurrently. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Junghans, Christoph |0 P:(DE-HGF)0 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Sutmann, Godehard |0 P:(DE-Juel1)132274 |b 2 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.parco.2023.103033 |g p. 103033 - |0 PERI:(DE-600)1466340-5 |p 103033 |t Parallel computing |v 117 |y 2023 |x 0167-8191 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1008596/files/MPCDKokkos.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1008596 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)132124 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)132274 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-13 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-13 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-08-23 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-08-23 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |d 2023-08-23 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b PARALLEL COMPUT : 2022 |d 2023-08-23 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-08-23 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-23 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-08-23 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-08-23 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2023-08-23 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|