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Working with models requires some consideration to avoid potential performance issues.

 

Hardware

The computer running the Visualisation Module should have high specifications with a dedicated graphics card to process data as efficiently as possible.

Consider changing the display mode if the model is lagging in response to user inputs.

 

Database

If performance issues are noted getting data from the database, review the layers that you require for the model and exclude any that are not important.  

If a layer is unlikely to change, the layer can be converted to modeled layer beforehand to maintain a static copy.  

 

Read / Write Speed

The speed of loading / saving a model will be affected by the model size and network connectivity.

With large models and/or slow network connectivity it may be beneficial to copy model files to the local device.

You will need to manually copy models back to the network after completing changes to the model.

 

Import / Export

To improve the import / export performance with slow network connectivity, run these processes on the local computer.

 

CAD Imports

When importing data from CAD files, avoid importing all data from a file if you only need one layer.  

We recommend opening CAD files in a viewer first to determine the layers to load.

 

Large Models

Performance issues commonly arise from the use of large images used in models, as these are stored in the model file to avoid reference issues.  

We recommend using the smallest possible image size to avoid potential issues.

 

Large Data

When working with large data it may be beneficial to minimize the size of model files.  

In this case the model file can be set to exclude database entities and rebuild these automatically from the database each time the file is opened.

For very large models, reducing the number of faces will improve rendering speed and reduce file size.

 

Slow Grid Regeneration

Grid Size greatly affects the speed of model regeneration, so using a large value (low resolution) is recommended while developing models, changing this to small values (high resolution) when generating outputs.