VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-msupports 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.
+ The rendering module requires that you have a extension binding library and one rendering library. A windowing library is not needed expect for some optional tests.
| BUILD_SHARED_LIBS | Enabled by default. Build all VTK-m libraries as shared libraries. |
| CMAKE_BUILD_TYPE | This statically specifies what build type (configuration) will be built in this build tree. Possible values are empty, Debug, Release, RelWithDebInfo and MinSizeRel. This variable is only meaningful to single-configuration generators (such as make and Ninja). |
| CMAKE_INSTALL_PREFIX | Directory to install VTK-m into. |
| VTKm_ENABLE_EXAMPLES | Disabled by default. Turn on building of simple examples of using VTK-m. |
| VTKm_ENABLE_BENCHMARKS | Disabled by default. Turn on additional timing tests. |
| VTKm_ENABLE_CUDA | Disabled by default. Enable CUDA backend. |
| VTKm_CUDA_Architecture | Defaults to native. Specify what GPU architecture(s) to build CUDA code for, options include native, fermi, kepler, maxwell, and pascal. |
| VTKm_ENABLE_TBB | Disabled by default. Enable Intel Threading Building Blocks backend. |
| VTKm_ENABLE_TESTING | Enabled by default. Turn on header, unit, worklet, and filter tests. |
| VTKm_ENABLE_RENDERING | Enabled by default. Turn on the rendering module. |
| VTKm_USE_64BIT_IDS | Enabled by default. This is the size of integers used to index arrays, points, cells, etc. Use 64 bit precision when on, 32 bit precision when off. |
| VTKm_USE_DOUBLE_PRECISION | Disabled by default. Precision to use in floating point numbers when no other precision can be inferred. Use 64 bit precision when on, 32 bit precision when off. |
Learning
========
VTK-m offers numerous different ways to learn how to use the provided components.
If you are interested in a high level overview of VTK-m a good place to start
is with the IEEE Vis talk ["VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures"](http://m.vtk.org/images/2/29/VTKmVis2016.pptx) or the older and more technical
presentation
["VTK-m Overview for Intel Design Review"](http://m.vtk.org/images/a/a4/VTKmIntelMeet.pptx).
If you are interested in learning how to use the existing VTK-m codebase,
or how to integrate into your own project, we recommend reading "Part 1: Getting Started"
and "Part 2: Using VTK-m" of the [VTK-m Users Guide][].
If you want to contribute to VTK-m we recommend reading the following sections
of the [VTK-m Users Guide][].
+ "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 tags, opengl interop and custom device adapters .
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: