A high-performance computing framework for Monte Carlo ocean color simulations
| Autor: | José C. Cunha, Tamito Kajiyama, Davide D'Alimonte |
|---|---|
| Přispěvatelé: | NOVALincs, DI - Departamento de Informática |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
System
Neural-network 010504 meteorology & atmospheric sciences Meteorology Computer science Computer Networks and Communications Monte Carlo method 02 engineering and technology Large-scale 01 natural sciences European seas Theoretical Computer Science Hybrid Monte Carlo ocean color Scientific applications Earth simulator 0202 electrical engineering electronic engineering information engineering 14. Life underwater Kinetic Monte Carlo Meris data SDG 14 - Life Below Water uncertainty Monte Carlo simulation 0105 earth and related environmental sciences high-performance computing Parallel Computer Science Applications Computational Theory and Mathematics Ocean color Dynamic Monte Carlo method 020201 artificial intelligence & image processing Monte Carlo method in statistical physics Products Performance computing Algorithms Software Monte Carlo molecular modeling |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | The authors are grateful to Dr. Giuseppe Zibordi (E.C. Joint Research Centre, Italy) for his support from in situ marine radiometry perspectives. This study was supported by the Portuguese Foundation for Science and Technology (FCT/MEC) through the grant PEst-OE/EEI/UI0527/2011. Access to the Milipeia cluster (University of Coimbra, Portugal) was granted through the project 'Large-scale parallel Monte Carlo simulations for ocean colour applications.' Additional funding was granted through the ESA contract No. 22576/09/I-OL, ARG/003-025/1406/CIMA, and NOVA LINCS Ref. UID/CEC/04516/2013. This paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large-scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. publishersversion published |
| Databáze: | OpenAIRE |
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