Difference between revisions of "Alternate Run Modes:Alternate Run Modes"
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===Inset Models=== | ===Inset Models=== | ||
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| + | ===Programmatic Interaction=== | ||
| + | New for version 8.0, GSSHA now has two ways to be used programmatically. First, users can use the library and related files to use GSSHA programmatically within an environment such as Visual Studio. The GSSHA Library exposes the GSSHA simulation class and a few related functions. The second means is through the new GSSHA python package. It uses cython to bridge the GSSHA library into a python usable format. | ||
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Latest revision as of 17:12, 9 December 2024
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:
Contents
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.
Programmatic Interaction
New for version 8.0, GSSHA now has two ways to be used programmatically. First, users can use the library and related files to use GSSHA programmatically within an environment such as Visual Studio. The GSSHA Library exposes the GSSHA simulation class and a few related functions. The second means is through the new GSSHA python package. It uses cython to bridge the GSSHA library into a python usable format.
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
- 18.8 Working with the GSSHA DLL Library
- 18.9 Working with the GSSHA Python Interface
