ParaView/UsersGuide/Recommendations

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Avoiding Data Explosion

The pipeline model that ParaView presents is very convenient for exploratory visualization. The loose coupling between components provides a very flexible framework for building unique visualizations, and the pipeline structure allows you to tweak parameters quickly and easily.

The downside of this coupling is that it can have a larger memory footprint. Each stage of this pipeline maintains its own copy of the data. Whenever possible, ParaView performs shallow copies of the data so that different stages of the pipeline point to the same block of data in memory. However, any filter that creates new data or changes the values or topology of the data must allocate new memory for the result. If ParaView is filtering a very large mesh, inappropriate use of filters can quickly deplete all available memory. Therefore, when visualizing large datasets, it is important to understand the memory requirements of filters.

Please keep in mind that the following advice is intended only for when dealing with very large amounts of data and the remaining available memory is low. When you are not in danger of running out of memory, the following advice is not relevant.

When dealing with structured data, it is absolutely important to know what filters will change the data to unstructured. Unstructured data has a much higher memory footprint, per cell, than structured data because the topology must be explicitly written out. There are many filters in ParaView that will change the topology in some way, and these filters will write out the data as an unstructured grid, because that is the only dataset that will handle any type of topology that is generated. The following list of filters will write out a new unstructured topology in its output that is roughly equivalent to the input. These filters should never be used with structured data and should be used with caution on unstructured data.

  • Append Datasets
  • Append Geometry
  • Clean
  • Clean to Grid
  • Connectivity
  • D3
  • Delaunay 2D/3D
  • Extract Edges
  • Linear Extrusion
  • Loop Subdivision
  • Reflect
  • Rotational Extrusion
  • Shrink
  • Smooth
  • Subdivide
  • Tessellate
  • Tetrahedralize
  • Triangle Strips
  • Triangulate

Technically, the Ribbon and Tube filters should fall into this list. However, as they only work on 1D cells in poly data, the input data is usually small and of little concern.

This similar set of filters also outputs unstructured grids, but also tends to reduce some of this data. Be aware though that this data reduction is often smaller than the overhead of converting to unstructured data. Also note that the reduction is often not well balanced. It is possible (often likely) that a single process may not lose any cells. Thus, these filters should be used with caution on unstructured data and extreme caution on structured data.

  • Clip
  • Decimate
  • Extract Cells by Region
  • Extract Selection
  • Quadric Clustering
  • Threshold

Similar to the items in the preceding list, Extract Subset performs data reduction on a structured dataset, but also outputs a structured dataset. So the warning about creating new data still applies, but you do not have to worry about converting to an unstructured grid.

This next set of filters also outputs unstructured data, but it also performs a reduction on the dimension of the data (for example 3D to 2D), which results in a much smaller output. Thus, these filters are usually safe to use with unstructured data and require only mild caution with structured data.

  • Cell Centers
  • Contour
  • Extract CTH Fragments
  • Extract CTH Parts
  • Extract Surface
  • Feature Edges
  • Mask Points
  • Outline (curvilinear)
  • Slice
  • Stream Tracer

The filters below do not change the connectivity of the data at all. Instead, they only add field arrays to the data. All the existing data is shallow copied. These filters are usually safe to use on all data.

  • Block Scalars
  • Calculator
  • Cell Data to Point Data
  • Curvature
  • Elevation
  • Generate Surface Normals
  • Gradient
  • Level Scalars
  • Median
  • Mesh Quality
  • Octree Depth Limit
  • Octree Depth Scalars
  • Point Data to Cell Data
  • Process Id Scalars
  • Random Vectors
  • Resample with dataset
  • Surface Flow
  • Surface Vectors
  • Texture Map to...
  • Transform
  • Warp (scalar)
  • Warp (vector)

This final set of filters either add no data to the output (all data of consequence is shallow copied) or the data they add is generally independent of the size of the input. These are almost always safe to add under any circumstances (although they may take a lot of time).

  • Annotate Time
  • Append Attributes
  • Extract Block
  • Extract Datasets
  • Extract Level
  • Glyph
  • Group Datasets
  • Histogram
  • Integrate Variables
  • Normal Glyphs
  • Outline
  • Outline Corners
  • Plot Global Variables Over Time
  • Plot Over Line
  • Plot Selection Over Time
  • Probe Location
  • Temporal Shift Scale
  • Temporal Snap-to-Time-Steps
  • Temporal Statistics

There are a few special case filters that do not fit well into any of the previous classes. Some of the filters, currently Temporal Interpolator and Particle Tracer, perform calculations based on how data changes over time. Thus, these filters may need to load data for two or more instances of time, which can double or more the amount of data needed in memory. The Temporal Cache filter will also hold data for multiple instances of time. Keep in mind that some of the temporal filters such as the Temporal Statistics and the filters that plot over time may need to iteratively load all data from disk. Thus, it may take an impractically long amount of time even if does not require any extra memory.

The Programmable Filter is also a special case that is impossible to classify. Since this filter does whatever it is programmed to do, it can fall into any one of these categories.


Culling Data

When dealing with large data, it is best to cull out data whenever possible and do so as early as possible. Most large data starts as 3D geometry and the desired geometry is often a surface. As surfaces usually have a much smaller memory footprint than the volumes that they are derived from, it is best to convert to a surface early on. Once you do that, you can apply other filters in relative safety.

A very common visualization operation is to extract isosurfaces from a volume using the Contour filter. The Contour filter usually outputs geometry much smaller than its input. Thus, the Contour filter should be applied early if it is to be used at all. Be careful when setting up the parameters to the Contour filter because it still is possible for it to generate a lot of data. which can happen if you specify many isosurface values. High frequencies such as noise around an isosurface value can also cause a large, irregular surface to form.

Another way to peer inside of a volume is to perform a Slice on it. The Slice filter will intersect a volume with a plane and allow you to see the data in the volume where the plane intersects. If you know the relative location of an interesting feature in your large dataset, slicing is a good way to view it.

If you have little a-priori knowledge of your data and would like to explore the data without the long memory and processing time for the full dataset, you can use the Extract Subset filter to subsample the data. The subsampled data can be dramatically smaller than the original data and should still be well load balanced. Of course, be aware that you may miss small features if the subsampling steps over them and that once you find a feature you should go back and visualize it with the full data set.

There are also several features that can pull out a subset of a volume: Clip, Threshold, Extract Selection, and Extract Subset can all extract cells based on some criterion. Be aware, however, that the extracted cells are almost never well balanced; expect some processes to have no cells removed. All of these filters, with the exception of Extract Subset, will convert structured data types to unstructured grids. Therefore, they should not be used unless the extracted cells are of at least an order of magnitude less than the source data.

When possible, replace the use of a filter that extracts 3D data with one that will extract 2D surfaces. For example, if you are interested in a plane through the data, use the Slice filter rather than the Clip filter. If you are interested in knowing the location of a region of cells containing a particular range of values, consider using the Contour filter to generate surfaces at the ends of the range rather than extract all of the cells with the Threshold filter. Be aware that substituting filters can have an effect on downstream filters. For example, running the Histogram filter after Threshold will have an entirely different effect then running it after the roughly equivalent Contour filter.