High-Performance Computing Applied in Project UBEST

被引:0
|
作者
Martins, Ricardo [1 ]
Rogeiro, Joao [1 ]
Rodrigues, Marta [1 ]
Fortunato, Andre B. [1 ]
Oliveira, Anabela [1 ]
Azevedo, Alberto [1 ]
机构
[1] LNEC Natl Lab Civil Engn, Hydraul & Environm Dept, Lisbon, Portugal
来源
BUSINESS INFORMATION SYSTEMS WORKSHOPS (BIS 2018) | 2019年 / 339卷
关键词
HPC; Estuaries; Numerical models; Parallel computing; Forecasts; SCHISM; UBEST;
D O I
10.1007/978-3-030-04849-5_44
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
UBEST aims at improving the global understanding of present and future biogeochemical buffering capacity of estuaries through the development of Observatories, computational web-portals that integrate field observation and real-time MPI (Message Passing Interface) numerical simulations. HPC (High-Performance Computing) is applied in Observatories to serve both on-the-fly frontend user requests for multiple spatial analyses and to speed up backend's forecast hydrodynamic and ecological simulations based on unstructured grids. Backend simulations are performed using the open-source SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model). Python programming language will be used in this project to automate the MPI simulations and the web-portal in Django.
引用
收藏
页码:507 / 516
页数:10
相关论文
共 50 条
  • [21] A Call to Action to Prepare the High-Performance Computing Workforce
    Lathrop, Scott
    COMPUTING IN SCIENCE & ENGINEERING, 2016, 18 (06) : 80 - 83
  • [22] High-performance computing and visualization of unsteady turbulent flows
    T. Kobayashi
    M. Tsubokura
    N. Oshima
    Journal of Visualization, 2008, 11 : 23 - 32
  • [23] Testing the Scalability of the HS-AUTOFIT Tool in a High-Performance Computing Environment
    Di Modica, Giuseppe
    Evangelisti, Luca
    Foschini, Luca
    Maris, Assimo
    Melandri, Sonia
    ELECTRONICS, 2021, 10 (18)
  • [24] A parallel computing architecture for high-performance OWL reasoning
    Quan, Zixi
    Haarslev, Volker
    PARALLEL COMPUTING, 2019, 83 : 34 - 46
  • [25] Parallel computing in bioinformatics: a view from high-performance, heterogeneous, and cloud computing
    Vega-Rodriguez, Miguel A.
    Santander-Jimenez, Sergio
    JOURNAL OF SUPERCOMPUTING, 2019, 75 (07) : 3369 - 3373
  • [26] Parallel computing in bioinformatics: a view from high-performance, heterogeneous, and cloud computing
    Miguel A. Vega-Rodríguez
    Sergio Santander-Jiménez
    The Journal of Supercomputing, 2019, 75 : 3369 - 3373
  • [27] Supporting High-Performance and High-Throughput Computing for Experimental Science
    Huerta E.A.
    Haas R.
    Jha S.
    Neubauer M.
    Katz D.S.
    Computing and Software for Big Science, 2019, 3 (1)
  • [28] High Performance Computing tools for the Integrated Tokamak Modelling project
    Guillerminet, B.
    Campos Plasencia, I.
    Haefele, M.
    Iannone, F.
    Jackson, A.
    Manduchi, G.
    Plociennik, M.
    Sonnendrucker, E.
    Strand, P.
    Owsiak, M.
    FUSION ENGINEERING AND DESIGN, 2010, 85 (3-4) : 388 - 393
  • [29] ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
    Dutta, Ritabrata
    Schoengens, Marcel
    Pacchiardi, Lorenzo
    Ummadisingu, Avinash
    Widmer, Nicole
    Kunzli, Pierre
    Onnela, Jukka-Pekka
    Mira, Antonietta
    JOURNAL OF STATISTICAL SOFTWARE, 2021, 100 (07): : 1 - 38
  • [30] Smart Job Scheduling for High-Performance Cloud Computing Services
    Muhtaroglu, N.
    Ari, I.
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING, 2011, 95