VTK-m

VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

You can find out more about the design of VTK-m on the VTK-m Wiki.

Learning Resources

• A high-level overview is given in the IEEE Vis talk "VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures."

• The VTK-m Users Guide provides extensive documentation. It is broken into multiple parts for learning and references at multiple different levels.

  • "Part 1: Getting Started" provides the introductory instruction for building VTK-m and using its high-level features.
  • "Part 2: Using VTK-m" covers the core fundamental components of VTK-m including data model, worklets, and filters.
  • "Part 3: Developing with VTK-m" covers how to develop new worklets and filters.
  • "Part 4: Advanced Development" covers topics such as new worklet types and custom device adapters.

• Community discussion takes place on the VTK-m users email list.

• Doxygen-generated nightly reference documentation is available online.

Contributing

There are many ways to contribute to VTK-m, with varying levels of effort.

• Ask a question on the VTK-m users email list.

• Submit new or add to discussions of a feature requests or bugs on the VTK-m Issue Tracker.

• Submit a Pull Request to improve VTK-m

  • See CONTRIBUTING.md for detailed instructions on how to create a Pull Request.
  • See the VTK-m Coding Conventions that must be followed for contributed code.

• Submit an Issue or Pull Request for the VTK-m Users Guide

Dependencies

VTK-m Requires:

• C++11 Compiler. VTK-m has been confirmed to work with the following
  • GCC 4.8+
  • Clang 3.3+
  • XCode 5.0+
  • MSVC 2015+
• CMake
  • CMake 3.3+ (for any build)
  • CMake 3.9+ (for CUDA build or OpenMP build)
  • CMake 3.11+ (for Visual Studio generator)

Optional dependencies are:

• CUDA Device Adapter
  • Cuda Toolkit 7.5+
• TBB Device Adapter
  • TBB
• OpenMP Device Adapter
  • Requires a compiler that supports OpenMP >= 4.0.
• OpenGL Rendering
  • The rendering module contains multiple rendering implementations including standalone rendering code. The rendering module also includes (optionally built) OpenGL rendering classes.
  • The OpenGL rendering classes require that you have a extension binding library and one rendering library. A windowing library is not needed except for some optional tests.
• Extension Binding
  • GLEW
• On Screen Rendering
  • OpenGL Driver
  • Mesa Driver
• On Screen Rendering Tests
  • GLFW
  • GLUT
• Headless Rendering
  • OS Mesa
  • EGL Driver

VTK-m has been tested on the following configurations:

• On Linux
  • GCC 4.8.5, 5.4.0, 6.4.0, Clang 3.8.0
  • CMake 3.9.2, 3.9.3, 3.10.3
  • CUDA 8.0.61, 9.1.85
  • TBB 4.4 U2, 2017 U7
• On Windows
  • Visual Studio 2015, 2017
  • CMake 3.3, 3.11.1
  • CUDA 9.1.85
  • TBB 2017 U3, 2018 U2
• On MacOS
  • AppleClang 6.0
  • TBB 2017 U6

Building

VTK-m supports all majors platforms (Windows, Linux, OSX), and uses CMake to generate all the build rules for the project. The VTK-m source code is available from the VTK-m download page or by directly cloning the VTK-m git repository.

$ git clone https://gitlab.kitware.com/vtk/vtk-m.git
$ mkdir vtkm-build
$ cd vtkm-build
$ cmake-gui ../vtk-m
$ make -j<N>
$ make test

A more detailed description of building VTK-m is available in the VTK-m Users Guide.

Example##

The VTK-m source distribution includes a number of examples. The goal of the VTK-m examples is to illustrate specific VTK-m concepts in a consistent and simple format. However, these examples only cover a small part of the capabilities of VTK-m.

Below is a simple example of using VTK-m to load a VTK image file, run the Marching Cubes algorithm on it, and render the results to an image:

vtkm::io::reader::VTKDataSetReader reader("path/to/vtk_image_file");
vtkm::cont::DataSet inputData = reader.ReadDataSet();
std::string fieldName = "scalars";

vtkm::Range range;
inputData.GetPointField(fieldName).GetRange(&range);
vtkm::Float64 isovalue = range.Center();

// Create an isosurface filter
vtkm::filter::MarchingCubes filter;
filter.SetIsoValue(0, isovalue);
filter.SetActiveField(fieldName);
vtkm::cont::DataSet outputData = filter.Execute(inputData);

// compute the bounds and extends of the input data
vtkm::Bounds coordsBounds = inputData.GetCoordinateSystem().GetBounds();
vtkm::Vec<vtkm::Float64,3> totalExtent( coordsBounds.X.Length(),
                                        coordsBounds.Y.Length(),
                                        coordsBounds.Z.Length() );
vtkm::Float64 mag = vtkm::Magnitude(totalExtent);
vtkm::Normalize(totalExtent);

// setup a camera and point it to towards the center of the input data
vtkm::rendering::Camera camera;
camera.ResetToBounds(coordsBounds);

camera.SetLookAt(totalExtent*(mag * .5f));
camera.SetViewUp(vtkm::make_Vec(0.f, 1.f, 0.f));
camera.SetClippingRange(1.f, 100.f);
camera.SetFieldOfView(60.f);
camera.SetPosition(totalExtent*(mag * 2.f));
vtkm::cont::ColorTable colorTable("inferno");

// Create a mapper, canvas and view that will be used to render the scene
vtkm::rendering::Scene scene;
vtkm::rendering::MapperRayTracer mapper;
vtkm::rendering::CanvasRayTracer canvas(512, 512);
vtkm::rendering::Color bg(0.2f, 0.2f, 0.2f, 1.0f);

// Render an image of the output isosurface
scene.AddActor(vtkm::rendering::Actor(outputData.GetCellSet(),
                                      outputData.GetCoordinateSystem(),
                                      outputData.GetField(fieldName),
                                      colorTable));
vtkm::rendering::View3D view(scene, mapper, canvas, camera, bg);
view.Initialize();
view.Paint();
view.SaveAs("demo_output.pnm");

License

VTK-m is distributed under the OSI-approved BSD 3-clause License. See LICENSE.txt for details.