Improving parallel performance of large-scale watershed simulations
Author(s) -
Paul R. Eller,
Jing-Ru C. Cheng,
Hung Viet Nguyen,
Robert S. Maier
Publication year - 2010
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2010.04.086
Subject(s) - computer science , watershed , scale (ratio) , computational science , distributed computing , parallel computing , machine learning , cartography , geography
A comprehensive, physics-based watershed model with multispatial domains and multitemporal scales has been developed and used. This paper discusses interfacing the watershed model with PETSc and evaluating the model performance for a variety of PETSc preconditioners. Both wall-clock time and scalability are compared based on performance on the Cray XT4 machine, along with tests to verify that all solutions are producing accurate results. The findings conclude that the PETSc Conjugate Gradient solver and preconditioners outperform the simple Conjugate Gradient solver and Jacobi preconditioner originally used by the watershed model. Tests show that the HypreBoomeramg preconditioner provides the most significant speedup for the watershed model
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