Abstract
For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only ∼2.5 hours (42 compute nodes)-a 40x speed-up. Tools developed for this parallel execution are discussed.
Original language | English |
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Pages (from-to) | 275-298 |
Number of pages | 24 |
Journal | GIScience and Remote Sensing |
Volume | 49 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1 2012 |
Funding
A. M. Filippi acknowledges that this research was supported in part by an appointment to the U.S. Department of Energy (DOE) Higher Education Research Experiences (HERE) for Faculty at the Oak Ridge National Laboratory (ORNL), administered by the Oak Ridge Institute for Science and Education. This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so,