A high-performance computing framework for Monte Carlo ocean color simulations

摘要

This paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations inrnthe ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer codernnamedMOX, developed by the authors to create a virtual marine environment for investigating the quality ofrnOC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set ofrnsolutions for performance modeling, prediction, and optimization is implemented to enhance the efficiencyrnof MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computingrntechniques are applied taking into account a clear separation and systematic treatment of accuracy and precisionrnrequirements for large-scale MC OC simulations. The added value of the work is the integration ofrncomputational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solvingrnenvironment specifically tailored to investigate data acquisition and reduction methods for OC field measurements.rnStudy results highlight the benefit of close collaboration between HPC and application domainrnresearchers to improve the efficiency and flexibility of computer simulations in the marine optics applicationrndomain.