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public:vievs_manual:gui_and_batch

# GUI and batch

A graphical user interface (GUI) for VieVS (see Fig. below), 'VIE_SETUP' is provided. All the different modules are integrated in the GUI and can be selected with the drop down menu. Additionally a plotting tool is implemented. The GUI can be started by typing vievs in the Matlab command window (be sure that the VieVS/WORK directory is set as current directory in Matlab).

## Processing preparation done by the GUI

The final purpose of the interface before processing is to save the required fi les at their required destination ('prepare processing'). The only difference between the buttons Save runp and Save + Run is that the latter also starts the processing (VIE_BATCH). Following files are created when clicking either Save runp or Save + Run:

• input_protocol.txt - not required for processing (see parameter logfi le)
• runp.mat - required in folder /WORK/ (see runp)
• process_list.mat - required in folder /WORK/ (see process lists)
• parameter.mat - required in folder /DATA/LEVEL0/subdir/ (see parameter files)
• Scheduling parameters - required for VIE_SCHED
• Simulation parameters - required for VIE_SIM
• Global parameters - required for VIE_GLOB

## Batch processing

To start the processing, the user has to click either on Save + Run in the GUI or type vievs('batch') in the command window (a runp.mat file has to be created beforehand). The chart below shows the workflow of VIE_BATCH. You can see the required input files on the left and the directory where intermediate and final results are saved on the right. In the middle you can see the modules which are used according to the input.

Note: the name vie_batch2_1 in this figure is only a placeholder for the current VieVS version

## Parallel computing

To speed up the processing of several sessions with the same parametrization, parallel computing, allows Matlab to use more than one core. Parallel computing can be enabled in Run - run options (see Figure below). The Parallel Computing Toolbox in Matlab is required to run parallel jobs.

When parallel computing is enabled, the sessions run in a parfor instead of a for loop. This decreases the computation time roughly by a factor of the number of cores (usually 2 or 4). If the number of cores in the GUI is set to auto, Matlab uses the number of pools speci ed by the default parallel con guration.