guest ::
| Home > Publications database > Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs) > print |
| Home > Publications database > Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs) > print |
| 001 | 907140 | ||
| 005 | 20240712100917.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-15-2731-2022 |2 doi |
| 024 | 7 | _ | |a 1991-959X |2 ISSN |
| 024 | 7 | _ | |a 1991-9603 |2 ISSN |
| 024 | 7 | _ | |a 2128/31014 |2 Handle |
| 024 | 7 | _ | |a altmetric:125957377 |2 altmetric |
| 024 | 7 | _ | |a WOS:000780834800001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-01863 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Hoffmann, Lars |0 P:(DE-Juel1)129125 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs) |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2022 |b Copernicus |
| 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 1649424371_9014 |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 Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become rather numerically costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the Message Passing Interface (MPI) – Open Multi-Processing (OpenMP) – OpenACC hybrid parallelization of MPTRAC were conducted on the Jülich Wizard for European Leadership Science (JUWELS) Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop s−1. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' fifth-generation reanalysis (ERA5), the performance evaluation showed a maximum speed-up of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model. |
| 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 |
| 536 | _ | _ | |a 2112 - Climate Feedbacks (POF4-211) |0 G:(DE-HGF)POF4-2112 |c POF4-211 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Baumeister, Paul F. |0 P:(DE-Juel1)156619 |b 1 |
| 700 | 1 | _ | |a Cai, Zhongyin |0 P:(DE-Juel1)180878 |b 2 |
| 700 | 1 | _ | |a Clemens, Jan |0 P:(DE-Juel1)180256 |b 3 |
| 700 | 1 | _ | |a Griessbach, Sabine |0 P:(DE-Juel1)129121 |b 4 |
| 700 | 1 | _ | |a Günther, Gebhard |0 P:(DE-Juel1)129123 |b 5 |
| 700 | 1 | _ | |a Heng, Yi |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Liu, Mingzhao |0 P:(DE-Juel1)187051 |b 7 |
| 700 | 1 | _ | |a Haghighi Mood, Kaveh |0 P:(DE-Juel1)176293 |b 8 |
| 700 | 1 | _ | |a Stein, Olaf |0 P:(DE-Juel1)3709 |b 9 |
| 700 | 1 | _ | |a Thomas, Nicole |0 P:(DE-Juel1)129162 |b 10 |
| 700 | 1 | _ | |a Vogel, Bärbel |0 P:(DE-Juel1)129164 |b 11 |
| 700 | 1 | _ | |a Wu, Xue |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Zou, Ling |0 P:(DE-Juel1)176891 |b 13 |
| 773 | _ | _ | |a 10.5194/gmd-15-2731-2022 |g Vol. 15, no. 7, p. 2731 - 2762 |0 PERI:(DE-600)2456725-5 |n 7 |p 2731 - 2762 |t Geoscientific model development |v 15 |y 2022 |x 1991-959X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/907140/files/gmd-15-2731-2022.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:907140 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)129125 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)156619 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)180878 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)180256 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)129121 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)129123 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)187051 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)176293 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)3709 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-Juel1)129162 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 11 |6 P:(DE-Juel1)129164 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 13 |6 P:(DE-Juel1)176891 |
| 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 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-211 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Die Atmosphäre im globalen Wandel |9 G:(DE-HGF)POF4-2112 |x 1 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-01-26 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-01-26 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2021-01-26 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2021-01-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2022-11-25 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-01-16T18:00:10Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-01-16T18:00:10Z |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Peer review |d 2021-01-16T18:00:10Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2022-11-25 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2022-11-25 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2022-11-25 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b GEOSCI MODEL DEV : 2021 |d 2022-11-25 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2022-11-25 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2022-11-25 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2022-11-25 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b GEOSCI MODEL DEV : 2021 |d 2022-11-25 |
| 915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
| 915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
| 915 | p | c | |a DFG OA Publikationskosten |2 APC |0 PC:(DE-HGF)0002 |
| 915 | p | c | |a DOAJ Journal |2 APC |0 PC:(DE-HGF)0003 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IEK-7-20101013 |k IEK-7 |l Stratosphäre |x 1 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IEK-7-20101013 |
| 980 | _ | _ | |a APC |
| 981 | _ | _ | |a I:(DE-Juel1)ICE-4-20101013 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|