Finally, the construction of topological information, such as cell links (which enable fast queries such as what cells use a particular point, or face neighbors to a 3D cell), has been threaded. In addition, algorithm accelerator structures such as point and cell locators have been parallel threaded, significantly speeding up common geometric search queries such as finding the closest point or searching for the cell containing a point. Recent blogs have described ways to utilize templated code, dispatch execution based on data type, and efficiently access VTK data arrays. The performance gains we realized were not due to any one technology, rather the methodical development and combination of several emerging capabilities as described in the following. Through VTK-m, and even web deployment via VTK.js, this blog focuses on high-performance C++ components implemented on threaded CPUs in the VTK toolkit and deployed via the ParaView application. While past efforts have addressed rendering performance improvements, GPU accelerator performance Several, combined approaches were used to accelerate many VTK algorithms (or filters).
#Paraview vtk software
Kitware remains committed to an open source business model, enabling its customers and collaborators to avoid vendor lock-in, reap the benefits of open technologies, and readily adapt and integrate this software into their own workflows and systems. Since VTK and ParaView are open source software systems, Kitware’s collaborators, community members, and customers – both commercial and governmental – have contributed guidance, feedback, and support for this work. In this post, we provide a high-level overview of how we achieved these gains and some numerical comparisons on a selection of the more important data visualization algorithms.īefore diving into the technical details, it should be noted that this effort has been a community effort (checking the git logs makes this plainly evident). Some of these have been dramatic – for example, when isocontouring linear unstructured grids speedups greater than 400x have been measured. Simple: since most ParaView programmable filter developers won’t use most of VTK’s Python modules, importing all of them is not only wasteful but also can slow things down considerably, especially when running on large number of MPI ranks in parallel on shared file system.Over the last few years, significant performance improvements have been introduced into VTK and ParaView. Now you may be wondering: w hy this complication? Thus, you have to explicitly import the class from the module in which it’s defined. To get to ‘vtkSphereSource’ from ‘paraview.vtk’, you can use the following syntax: from import vtkSphereSource Vtk.vtkSphereSource() # - This raises 'AttributeError' :(
However the following doesn’t: from paraview import vtk To clarify things, let’s look at a simple example: import vtk
What’s different is which modules are loaded and hence what classes are available within the vtk package itself. vtk.vtkCommonCore, vtk.vtkCommonDataModel etc. In both cases, the vtk package has similar submodules eg. # Option 2: Import the 'vtk' package within 'paraview' for use in Programmable Source or Programmable Filter, will soon notice that there are two ways of importing the vtk package: # Option 1: import the 'vtk' package directly. Any ParaView user (and developer) that is writing Python scripts in ParaView for data processing i.e.
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