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Robert Maynard 047b646517 VTK-m now provides better scheduling parameters controls
VTK-m now offers a more GPU aware set of defaults for kernel scheduling.
When VTK-m first launches a kernel we do system introspection and determine
what GPU's are on the machine and than match this information to a preset
table of values. The implementation is designed in a way that allows for
VTK-m to offer both specific presets for a given GPU ( V100 ) or for
an entire generation of cards ( Pascal ).

Currently VTK-m offers preset tables for the following GPU's:
- Tesla V100
- Tesla P100

If the hardware doesn't match a specific GPU card we than try to find the
nearest know hardware generation and use those defaults. Currently we offer
defaults for
- Older than Pascal Hardware
- Pascal Hardware
- Volta+ Hardware

Some users have workloads that don't align with the defaults provided by
VTK-m. When that is the cause, it is possible to override the defaults
by binding a custom function to `vtkm::cont::cuda::InitScheduleParameters`.
As shown below:

```cpp
  ScheduleParameters CustomScheduleValues(char const* name,
                                          int major,
                                          int minor,
                                          int multiProcessorCount,
                                          int maxThreadsPerMultiProcessor,
                                          int maxThreadsPerBlock)
  {

    ScheduleParameters params  {
        64 * multiProcessorCount,  //1d blocks
        64,                        //1d threads per block
        64 * multiProcessorCount,  //2d blocks
        { 8, 8, 1 },               //2d threads per block
        64 * multiProcessorCount,  //3d blocks
        { 4, 4, 4 } };             //3d threads per block
    return params;
  }
  vtkm::cont::cuda::InitScheduleParameters(&CustomScheduleValues);
```
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benchmarking Add support for BitFields. 2019-04-11 08:27:17 -04:00
CMake Always export vtkm_cuda no matter VTKm_INSTALL_ONLY_LIBRARIES 2019-04-12 11:10:10 -04:00
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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.

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 3.8+ (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
  • TBB Device Adapter
  • 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
  • On Screen Rendering
    • OpenGL Driver
    • Mesa Driver
  • On Screen Rendering Tests
  • Headless Rendering

VTK-m has been tested on the following configurations:c

  • On Linux
    • GCC 4.8.5, 5.4.0, 6.4.0, 7.3.0, Clang 5.0, 6.0, 7.0, Intel 17.0.4
    • CMake 3.13.3, 3.14.1
    • CUDA 9.2.148, 10.0.130, 10.1.105
    • TBB 4.4 U2, 2017 U7
  • On Windows
    • Visual Studio 2015, 2017
    • CMake 3.8.2, 3.11.1, 3.12.4
    • CUDA 10.1
    • TBB 2017 U3, 2018 U2
  • On MacOS
    • AppleClang 9.1
    • CMake 3.12.3
    • TBB 2018

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.