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Parallel Simulation in Subsurface Hydrology: Evaluating the Performance of Modeling Computers.
Theo S Sarris, David M Scott1, Murray E Close1
1Institute of Environmental Science and Research (ESR), Christchurch, New Zealand.
Parallel processing speeds up hydrological modeling by reducing simulation time. Optimal performance requires more processes than CPU cores, especially for complex simulations.
Area of Science:
- Environmental science
- Computational hydrology
- High-performance computing
Background:
- Hydrological modeling, including Monte Carlo uncertainty analysis, often faces computational bottlenecks due to numerous model simulations.
- Parallel processing offers a solution to reduce simulation time by leveraging modern computer architectures.
Purpose of the Study:
- To investigate the performance of parallel simulations in hydrology across different hardware setups.
- To provide insights into expected performance and inform hardware investment decisions for parallel modeling.
Main Methods:
- Two realistic flow and transport modeling scenarios were used to test system performance.
- Performance was measured in terms of speedup and efficiency as the number of parallel processes increased.
Main Results:
- Maximum parallelization performance ranged from 40% to 100% of the theoretical limit, with multi-CPU servers showing lower gains.
- The optimal number of parallel processes to maximize performance is application-dependent and often exceeds the total number of system CPU cores.
Conclusions:
- Further research is needed to understand the impact of physical problem characteristics on the optimal number of parallel processes.
- When using laptops for modeling, consider both specifications and manufacturer-designated use for effective parallel simulation.
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