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
  • [31] A Design for Multi-Pricing High-Performance Computing System
    Chen, Lung-Pin
    Kao, Mike
    Wu, I-Chen
    Wei, Ting-Han
    INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014), 2015, 274 : 1733 - 1742
  • [32] High-Performance Computing (HPC): Application & Use in the Power Grid
    Chavarria-Miranda, Daniel
    Huang, Zhenyu
    Chen, Yousu
    2012 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2012,
  • [33] 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
  • [34] A Constraint Programming Scheduler for Heterogeneous High-Performance Computing Machines
    Bridi, Thomas
    Bartolini, Andrea
    Lombardi, Michele
    Milano, Michela
    Benini, Luca
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2016, 27 (10) : 2781 - 2794
  • [35] Frequency Recovery in Power Grids using High-Performance Computing
    Rao, Vishwas
    Subramanyam, Anirudh
    Schanen, Michel
    Kim, Youngdae
    Satkauskas, Ignas
    Anitescu, Mihai
    51ST INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS PROCEEDINGS, ICPP 2022, 2022,
  • [36] 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
  • [37] Reliability-oriented resource management for High-Performance Computing
    Massari, Giuseppe
    Peta, Miriam
    Campi, Alessandro
    Reghenzani, Federico
    Terraneo, Federico
    Agosta, Giovanni
    Fornaciari, William
    Ciesielski, Sebastian
    Kulczewski, Michal
    Piatek, Wojciech
    SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2023, 39
  • [38] A High-Performance Parallel Approach to Image Processing in Distributed Computing
    Rakhimov, Mekhriddin
    Mamadjanov, Doniyor
    Mukhiddinov, Abulkosim
    2020 IEEE 14TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT2020), 2020,
  • [39] A Heterogeneous Supercomputer Model for High-Performance Parallel Computing Pedagogy
    Wolfer, James
    PROCEEDINGS OF 2015 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON), 2015, : 799 - 805
  • [40] Research Computing on Campus - Application of a Production Function to the Value of Academic High-Performance Computing
    Smith, Preston
    Harrell, Stephen Lien
    PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING 2021, PEARC 2021, 2021,