Shuenhoy/vtk-m2
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Kenneth Moreland 72b43d7151 Consolidate background color in rendering classes 
Before this commit, there were three separate classes (Mapper, Canvas,
and View) that were all managing their own version of the background
color. As you can imagine, this could easily become out of sync, and in
fact if the user code did not specify the same background at least
twice, it would not work.

Fix this by consolidating the background color management to the Canvas.
This is the class most responsible for maintaining the background. All
other classes get or set the background from the Canvas.

That said, I also removed setting the background color from the
constructor in the Canvas. This background color is overridden by the
View anyway, so having it there was only confusing.
2016-06-11 12:09:51 -06:00
CMake Remove debug message from UseVTKmCUDA.cmake 2016-06-10 13:33:07 -04:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Consolidate background color in rendering classes 2016-06-11 12:09:51 -06:00
vtkm Consolidate background color in rendering classes 2016-06-11 12:09:51 -06:00
CMakeLists.txt Add test to check that all source files are part of the build system 2016-06-02 10:23:37 -06:00
CONTRIBUTING.md Add a contributing guide to vtk-m. 2015-07-29 17:33:30 -04:00
CTestConfig.cmake Switch over to uploading by https as that is required by cdash. 2016-02-23 14:03:52 -05:00
CTestCustom.cmake.in Lossen the CTestCustom regexes 2016-03-18 13:46:31 -04:00
LICENSE.txt Fix compile time errors 2015-08-21 11:17:10 -07:00
README.md Update ReadMe to reference gitlab. 2015-05-13 08:45:52 -04:00

README.md

VTK-m

One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential.

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.

Getting VTK-m

The VTK-m repository is located at https://gitlab.kitware.com/vtk/vtk-m

VTK-m dependencies are:

git clone https://gitlab.kitware.com/vtk/vtk-m.git vtkm
mkdir vtkm-build
cd vtkm-build
cmake-gui ../vtkm

A detailed walk-through of installing and building VTK-m can be found on our Contributing page