Difference between revisions of "Alternate Run Modes:Alternate Run Modes"

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*Calibration - Run GSSHA using the Shuffled Complex Evolution calibration routine as the controller. Can only run in serial mode. Use -c command line option.
 
*Calibration - Run GSSHA using the Shuffled Complex Evolution calibration routine as the controller. Can only run in serial mode. Use -c command line option.
 
*Monte Carlo - Run GSSHA using a Monte Carlo calibration routine as the controller. Can run in serial, OpenMP, or MPI mode. Use -m command line option.
 
*Monte Carlo - Run GSSHA using a Monte Carlo calibration routine as the controller. Can run in serial, OpenMP, or MPI mode. Use -m command line option.
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*Efficient Local Search - Run GSSHA using the Levenberg-Marquardt (LM) local search method, or the Secant LM (SLM) method, an efficiency enhancement to the LM method, calibration routine as the controller. Use -slm command line option.
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*Multistart - Run GSSHA using the Multistart stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -ms command line option.
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*Trajectory Repulsion - Run GSSHA using the Trajectory Repulsion stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -tr command line option.
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*Effective and Efficient Stochastic Global Optimization - Run GSSHA using the Multilevel Single Linkage (MLSL) stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -mlsl command line option.
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===Inset Models===
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GSSHA is able to share data between individual models.
  
 
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Latest revision as of 19:26, 10 March 2023

There are several modes of running GSSHA. Each of these is for a particular situation, and some require specific hardware in order to be useful. Each of these alternate run modes is set from the command line when you run a project. These alternate run modes are:

Parallelization Models

  • OpenMP - Run a shared-memory parallelized version of GSSHA.
  • MPI - Run a distributed-memory parallelized version of GSSHA. Works on 32- or 64-bit Microsoft(R) Windows(R) machines. Must have the mpich routines installed and running. Compiled as a special exectable for 32- and 64-bit.

Control Models

  • Batch - Run several versions of a single simulation. Can run in serial, OpenMP, or MPI mode. Use -b command line option.
  • Calibration - Run GSSHA using the Shuffled Complex Evolution calibration routine as the controller. Can only run in serial mode. Use -c command line option.
  • Monte Carlo - Run GSSHA using a Monte Carlo calibration routine as the controller. Can run in serial, OpenMP, or MPI mode. Use -m command line option.
  • Efficient Local Search - Run GSSHA using the Levenberg-Marquardt (LM) local search method, or the Secant LM (SLM) method, an efficiency enhancement to the LM method, calibration routine as the controller. Use -slm command line option.
  • Multistart - Run GSSHA using the Multistart stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -ms command line option.
  • Trajectory Repulsion - Run GSSHA using the Trajectory Repulsion stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -tr command line option.
  • Effective and Efficient Stochastic Global Optimization - Run GSSHA using the Multilevel Single Linkage (MLSL) stochastic global optimization calibration routine, which uses the LM/SLM method for local searches as the controller. Use -mlsl command line option.

Inset Models

GSSHA is able to share data between individual models.


GSSHA User's Manual

18 Alternate Run Modes
18.1     MPI and OpenMP Parallelization
18.2     Simulation Setup for Alternate Run Modes
18.3     Batch Mode Runs
18.4     Automated Calibration with Shuffled Complex Evolution
18.5     Monte Carlo Runs
18.6     ERDC Automated Model Calibration Software
   18.6.1     Efficient Local Search
   18.6.2     Multistart
   18.6.3     Trajectory Repulsion
   18.6.4     Effective and Efficient Stochastic Global Optimization
18.7     Inset Models