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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.