A Python multiprocessing approach for fast geostatistical simulations of subglacial topography
نویسندگان
چکیده
Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding basal processes and quantifying uncertainty in sea-level rise projections with respect to topographic uncertainty. This can be achieved Sequential Gaussian Simulation (SGS), which is used generate multiple non-unique geological phenomena that sample the space. However, SGS very CPU intensive a computational complexity O( Nk 3 ), where xmlns:xlink="http://www.w3.org/1999/xlink">N number grid cells simulate, xmlns:xlink="http://www.w3.org/1999/xlink">k neighboring points conditioning. makes prohibitively time-consuming implement at ice-sheet scales or fine resolutions. To reduce time-cost, we test multiprocess version using Python’s multiprocessing module. By parallelizing calculation weight parameters SGS, achieve speedup 9.5 running on 16 processors an 128,097. speedup, as well from processors, increases . speed improvement viable large-scale mapping ensemble modeling. Additionally, have made code repository user tutorials publicly available (GitHub, Zenodo others use implementation different datasets.
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ژورنال
عنوان ژورنال: Computing in Science and Engineering
سال: 2023
ISSN: ['1558-366X', '1521-9615']
DOI: https://doi.org/10.1109/mcse.2023.3317773