Go to file
Vicente Adolfo Bolea Sanchez d348b11183 Enable shared CUDA builds when not compiling virtuals
The reason why we did not support shared libraries when CUDA compiles
were on is that virtual methods require a special linking step to pull
together all virtual methods that might be called. I other words, you
cannot call a virtual CUDA method defined inside a library. This
requirement goes away when virtuals are removed.

Also removed the necessity of using seprable compilation with cuda.
Again, this is only needed when a CUDA function is defined in one
translation unit and used in another. Now we can enforce that all
translation units define their own CUDA functions.

Also, suppress warnings in cuda/internal/ExecutionPolicy.h

This is where we call the thrust algorithms. There must be some loop
where it, on some code path, calls back a host function. This must be in
an execution path that never happens. The thrust version has its own
suppress, but that does not seem to actually suppress the warning (it
just means that the warning does not tell you where the actual call is).

Get around the problem by suppressing the warnings in VTK-m.

Co-authored-by: Kenneth Moreland <morelandkd@ornl.gov>
Co-authored-by: Vicente Adolfo Bolea Sanchez <vicente.bolea@kitware.com>

Signed-off-by: Vicente Adolfo Bolea Sanchez <vicente.bolea@kitware.com>
2021-08-24 13:14:58 -04:00
.github docs: update gitlab links to include /-/ component 2020-05-26 14:48:49 -04:00
.gitlab Enable shared CUDA builds when not compiling virtuals 2021-08-24 13:14:58 -04:00
benchmarking Make benchmarks work with most recent TBB 2021-06-15 10:48:48 -06:00
CMake Enable shared CUDA builds when not compiling virtuals 2021-08-24 13:14:58 -04:00
config added pkg-config .pc 2021-07-23 15:33:07 -06:00
data Add test file for corner case. 2021-08-03 15:33:51 -04:00
docs Add ability to convert fields to known types 2021-08-19 07:10:20 -06:00
examples Fix deprecation warning in histogram example 2021-08-06 06:53:29 -06:00
Utilities LFS: Set default url to the current origin url 2021-07-16 16:18:10 -04:00
vtkm Enable shared CUDA builds when not compiling virtuals 2021-08-24 13:14:58 -04:00
vtkmstd Have VTKM_IS_TRIVIAL* macros show types better 2021-04-02 07:37:26 -06:00
.clang-format clang-format: update configuration for 9.0 2020-08-24 11:47:55 -04:00
.gitattributes clang-format: update configuration for 9.0 2020-08-24 11:47:55 -04:00
.gitignore Add a point-oscillator filter + example 2018-07-18 09:33:06 -04:00
.gitlab-ci.yml Enable shared CUDA builds when not compiling virtuals 2021-08-24 13:14:58 -04:00
.hooks-config hooks: add hook chains for development checks and LFS 2020-04-02 12:51:43 -04:00
.kitware-release.json CI: adds .kitware-release.json 2021-05-24 18:26:39 -04:00
.lfsconfig LFS: Set lfs.url upon the origin url 2021-06-24 13:26:40 -04:00
CMakeLists.txt Enable shared CUDA builds when not compiling virtuals 2021-08-24 13:14:58 -04:00
CONTRIBUTING.md docs: update gitlab links to include /-/ component 2020-05-26 14:48:49 -04:00
CTestConfig.cmake conslidate the license statement 2019-04-17 10:57:13 -06:00
CTestCustom.cmake.in Turn on CUDA warnings for unknown stack sizes 2021-08-02 09:50:41 -06:00
LICENSE.txt Update the date on the license 2021-08-23 12:51:52 -06:00
README.md cmake: set c++14 as minimum c++ rev 2021-02-08 17:13:05 +01:00
version.txt 1.6.0 is our 8th official release of VTK-m. 2021-05-28 17:38:09 -04:00

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.
  • A practical VTK-m Tutorial based in what users want to accomplish with VTK-m:

    • Building VTK-m and using existing VTK-m data structures and filters.
    • Algorithm development with VTK-m.
    • Writing new VTK-m filters.
  • 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 5.4+
    • Clang 5.0+
    • XCode 5.0+
    • MSVC 2015+
    • Intel 17.0.4+
  • CMake
    • CMake 3.12+
    • CMake 3.13+ (for CUDA support)

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 5.4.0, 5.4, 6.5, 7.4, 8.2, 9.2; Clang 5, 8; Intel 17.0.4; 19.0.0
    • CMake 3.12, 3.13, 3.16, 3.17
    • CUDA 9.2, 10.2, 11.0, 11.1
    • TBB 4.4 U2, 2017 U7
  • On Windows
    • Visual Studio 2015, 2017
    • CMake 3.12, 3.17
    • CUDA 10.2
    • TBB 2017 U3, 2018 U2
  • On MacOS
    • AppleClang 9.1
    • CMake 3.12
    • 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.

The basic procedure for building VTK-m is to unpack the source, create a build directory, run CMake in that build directory (pointing to the source) and then build. Here are some example *nix commands for the process (individual commands may vary).

$ tar xvzf ~/Downloads/vtk-m-v1.4.0.tar.gz
$ mkdir vtkm-build
$ cd vtkm-build
$ cmake-gui ../vtk-m-v1.4.0
$ cmake --build -j .              # Runs make (or other build program)

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:

#include <vtkm/Bounds.h>
#include <vtkm/Range.h>
#include <vtkm/cont/ColorTable.h>
#include <vtkm/filter/Contour.h>
#include <vtkm/io/VTKDataSetReader.h>
#include <vtkm/rendering/Actor.h>
#include <vtkm/rendering/Camera.h>
#include <vtkm/rendering/CanvasRayTracer.h>
#include <vtkm/rendering/Color.h>
#include <vtkm/rendering/MapperRayTracer.h>
#include <vtkm/rendering/Scene.h>
#include <vtkm/rendering/View3D.h>

vtkm::io::VTKDataSetReader reader("path/to/vtk_image_file.vtk");
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::Contour 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();

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

A minimal CMakeLists.txt such as the following one can be used to build this example.

project(example)

set(VTKm_DIR "/somepath/lib/cmake/vtkm-XYZ")

find_package(VTKm REQUIRED)

add_executable(example example.cxx)
target_link_libraries(example vtkm_cont vtkm_rendering)

License

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