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. | ||
| + | *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. | ||
<noinclude> | <noinclude> | ||
{{Nav|Nav18}} | {{Nav|Nav18}} | ||
</noinclude> | </noinclude> | ||
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
