{"title":"Multistage Iterative Method to Tackle Inverse Problems of Wave Tomography","authors":"A. Goncharsky, S. Romanov, S. Seryozhnikov","doi":"10.14529/jsfi220106","DOIUrl":"https://doi.org/10.14529/jsfi220106","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"23 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122238179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Shutyaev, V. Agoshkov, V. Zalesny, E. Parmuzin, N. Zakharova
{"title":"4D Technology of Variational Data Assimilation for Sea Dynamics Problems","authors":"V. Shutyaev, V. Agoshkov, V. Zalesny, E. Parmuzin, N. Zakharova","doi":"10.14529/jsfi220101","DOIUrl":"https://doi.org/10.14529/jsfi220101","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129279496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The land surface model (LSM) is a necessary compartment of any numerical weather forecast system or the Earth system model. This paper presents a new version of the INM RAS-MSU land surface model where the river hydrodynamic and thermodynamic scheme is embedded into the parallel execution framework using MPI and OpenMP. Numerical experiments have been performed for the East European domain with resolution 0 . 5 ◦ × 0 . 5 ◦ . The soil model parallel efficiency at 1–144 MPI cores was 0.52–0.79 and limited by the presence of ocean area, and by imbalance of computational load between soil columns. The acceleration of the river model at MPI level was defined by the size of the largest river basin in the domain. At the OpenMP level, the potential for acceleration of large river basin simulation is shown to be close to number of threads used, based on fractal properties of the river networks. This acceleration was hindered in our numerical experiments by the reduced river orders at the coarse land surface model resolution, so that the optimal speedup for the Volga river basin was 2.5–3 times attained at 4–6 threads. This performance is projected to improve with refinement of the LSM spatial resolution. This paper presents a new version of the INM land where the river hydrodynamic and thermodynamic model is embedded into the parallel execution framework using two levels of parallelism: the first is MPI-based indepedent processing of river basins, and the second uses OpenMP technique to parallelize the simulation of rivers of the same Strahler order. Numerical experiments have been performed for the East European domain with resolution 0 . 5 ◦ × 0 . 5 ◦ . The MPI implementation of the soil model is based on conventional even longitude-latitude decomposition of the model domain, inherited from the atmospheric model. The soil model parallel efficiency at 1–144 cores was shown to be 0.52–0.79 and limited by the presence of ocean area, and by imbalance of computational load between soil columns depending on the presence of snow cover and number of iterations for the surface temperature needed to advance the soil profiles. The acceleration of the river model at MPI level (not exceeding 4 times) is defined by the size of the largest river basin in the domain (Volga), whereas at OpenMP level the potential for acceleration of large river basin simulation is shown to be close to number of threads used. OpenMP-level speedup was hindered in our numerical experiments by the underestimation of river orders at coarse land surface model resolution (recommended performance for the Volga basin attained at 4–6 threads with 2.5–3 times acceleration).
{"title":"River Routing in the INM RAS-MSU Land Surface Model: Numerical Scheme and Parallel Implementation on Hybrid Supercomputers","authors":"V. Stepanenko","doi":"10.14529/jsfi220103","DOIUrl":"https://doi.org/10.14529/jsfi220103","url":null,"abstract":"The land surface model (LSM) is a necessary compartment of any numerical weather forecast system or the Earth system model. This paper presents a new version of the INM RAS-MSU land surface model where the river hydrodynamic and thermodynamic scheme is embedded into the parallel execution framework using MPI and OpenMP. Numerical experiments have been performed for the East European domain with resolution 0 . 5 ◦ × 0 . 5 ◦ . The soil model parallel efficiency at 1–144 MPI cores was 0.52–0.79 and limited by the presence of ocean area, and by imbalance of computational load between soil columns. The acceleration of the river model at MPI level was defined by the size of the largest river basin in the domain. At the OpenMP level, the potential for acceleration of large river basin simulation is shown to be close to number of threads used, based on fractal properties of the river networks. This acceleration was hindered in our numerical experiments by the reduced river orders at the coarse land surface model resolution, so that the optimal speedup for the Volga river basin was 2.5–3 times attained at 4–6 threads. This performance is projected to improve with refinement of the LSM spatial resolution. This paper presents a new version of the INM land where the river hydrodynamic and thermodynamic model is embedded into the parallel execution framework using two levels of parallelism: the first is MPI-based indepedent processing of river basins, and the second uses OpenMP technique to parallelize the simulation of rivers of the same Strahler order. Numerical experiments have been performed for the East European domain with resolution 0 . 5 ◦ × 0 . 5 ◦ . The MPI implementation of the soil model is based on conventional even longitude-latitude decomposition of the model domain, inherited from the atmospheric model. The soil model parallel efficiency at 1–144 cores was shown to be 0.52–0.79 and limited by the presence of ocean area, and by imbalance of computational load between soil columns depending on the presence of snow cover and number of iterations for the surface temperature needed to advance the soil profiles. The acceleration of the river model at MPI level (not exceeding 4 times) is defined by the size of the largest river basin in the domain (Volga), whereas at OpenMP level the potential for acceleration of large river basin simulation is shown to be close to number of threads used. OpenMP-level speedup was hindered in our numerical experiments by the underestimation of river orders at coarse land surface model resolution (recommended performance for the Volga basin attained at 4–6 threads with 2.5–3 times acceleration).","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"388 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116657524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Starchenko, E. Danilkin, Sergei A. Prokhanov, L. I. Kizhner, E. Shelmina
{"title":"A Supercomputer-Based Modeling System for Short-Term Prediction of Urban Surface Air Quality","authors":"A. Starchenko, E. Danilkin, Sergei A. Prokhanov, L. I. Kizhner, E. Shelmina","doi":"10.14529/jsfi220102","DOIUrl":"https://doi.org/10.14529/jsfi220102","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116516274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fábio Porto, Mariza Ferro, Eduardo S. Ogasawara, T. Moeda, Claudio D. T. Barros, A. Silva, Rocío Zorrilla, R. S. Pereira, Rafaela Nascimento Castro, João Victor Silva, Rebecca Salles, Augusto Fonseca, Juliana Hermsdorff, Marcelo Magalhães, Vitor Sá, Adolfo Simões, Carlos Cardoso, Eduardo Bezerra
{"title":"Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results","authors":"Fábio Porto, Mariza Ferro, Eduardo S. Ogasawara, T. Moeda, Claudio D. T. Barros, A. Silva, Rocío Zorrilla, R. S. Pereira, Rafaela Nascimento Castro, João Victor Silva, Rebecca Salles, Augusto Fonseca, Juliana Hermsdorff, Marcelo Magalhães, Vitor Sá, Adolfo Simões, Carlos Cardoso, Eduardo Bezerra","doi":"10.14529/jsfi220104","DOIUrl":"https://doi.org/10.14529/jsfi220104","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126842582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Velho, H. M. Furtado, S. B. Sambatti, Carla Barros Osthoff Ferreira de Barros, M. E. Welter, R. Souto, D. Carvalho, D. O. Cardoso
{"title":"Data Assimilation by Neural Network for Ocean Circulation: Parallel Implementation","authors":"H. Velho, H. M. Furtado, S. B. Sambatti, Carla Barros Osthoff Ferreira de Barros, M. E. Welter, R. Souto, D. Carvalho, D. O. Cardoso","doi":"10.14529/jsfi220105","DOIUrl":"https://doi.org/10.14529/jsfi220105","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121217826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Direct Numerical Simulation of Stratified Turbulent Flows and Passive Tracer Transport on HPC Systems: Comparison of CPU Architectures","authors":"E. Mortikov, A. Debolskiy","doi":"10.14529/jsfi210405","DOIUrl":"https://doi.org/10.14529/jsfi210405","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"High-performance Shallow Water Model for Use on Massively Parallel and Heterogeneous Computing Systems","authors":"A. Chaplygin, A. Gusev, N. Diansky","doi":"10.14529/jsfi210407","DOIUrl":"https://doi.org/10.14529/jsfi210407","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"25 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129809744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Scalability as a Key Property of Mapping Computational Tasks to Supercomputer Architecture","authors":"A. Antonov","doi":"10.14529/jsfi210406","DOIUrl":"https://doi.org/10.14529/jsfi210406","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123449617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Influence of Autumn Eurasian Snow Cover on the Atmospheric Dynamics Anomalies during the Next Winter in INMCM5 Model Data","authors":"M. Tarasevich, E. Volodin","doi":"10.14529/jsfi210403","DOIUrl":"https://doi.org/10.14529/jsfi210403","url":null,"abstract":"","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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