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1. Identity statement
Reference TypeJournal Article
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Sitemtc-m21d.sid.inpe.br (namespace prefix: upn:44QHRCS)
Identifier8JMKD3MGP3W34T/465EPUS
Repositorysid.inpe.br/mtc-m21d/2022/01.04.17.46   (restricted access)
Last Update2022:01.04.17.46.36 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/01.04.17.46.36
Metadata Last Update2023:01.03.16.45.59 (UTC) administrator
DOI10.1016/j.envsoft.2021.105271
ISSN1364-8152
Citation KeyFreitasMendIlic:2022:PeOpMG
TitlePerformance optimization of the MGB hydrological model for multi-core and GPU architectures
Year2022
MonthFeb.
Access Date2026, Sep. 13
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size13327 KiB
2. Context
Author1 Freitas, Henrique Rennó de Azeredo
2 Mendes, Celso Luiz
3 Ilic, Aleksandar
Group1 DIOTG-CGCT-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade de Lisboa
Author e-Mail Address1 henrique.renno@inpe.br
2 celso.mendes@inpe.br
3 aleksandar.ilic@inesc-id.pt
JournalEnvironmental Modelling and Software
Volume148
Pagese105271
Secondary MarkA1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_III A1_ENGENHARIAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_CIÊNCIA_DA_COMPUTAÇÃO A2_ECONOMIA A2_BIODIVERSIDADE B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_ANTROPOLOGIA_/_ARQUEOLOGIA C_CIÊNCIAS_BIOLÓGICAS_I
Host Collectionurlib.net/www/2021/06.04.03.40 upn:44QHRCS
History (UTC)2022-01-04 17:46:36 :: simone -> administrator ::
2022-01-04 17:46:37 :: administrator -> simone :: 2022
2022-01-04 17:47:11 :: simone -> administrator :: 2022
2023-01-03 16:45:59 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsHigh performance computing
Hydrology models
Parallel processing
Parameterization
Roofline model
Vectorization
AbstractLarge-scale hydrological models simulate watershed processes with applications in water resources, climate change, land use, and forecast systems. The quality of the simulations mainly depends on calibrating optimal sets of watershed parameters, a time-consuming task that highly demands computational resources from repeated simulations. This work aims at performance optimizations on the MGB (Modelo de Grandes Bacias) hydrological model and the MOCOM-UA (Multi-Objective Complex Evolution) calibration method for two watersheds. The optimizations target state-of-the-art CPU/GPU systems, exploiting techniques that include AVX-512 vectorization, and multi-core (CPU) and many-core (GPU) parallelisms. Significant speedups of up to 20 × (CPU) were achieved for calibration, while the scalability analysis indicated 24 × (CPU) and 65 × (GPU) for simulations with larger problem sizes. The roofline analysis confirmed more effective use of the hardware resources, and the quantitative accuracy evaluation of the optimized implementations reached maximum relative errors of approximately 6% for discharges and objective functions.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Performance optimization of...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Filefreitas_performance.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.22.23 - 75
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
6. Notes
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