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dpugmire fe1ab945a2 This commit has several changes. 
1. Additional ASSERT calls to validate arguments in: DataSetBuilderRegular
2. Fix some untested compile errors in DataSetBuilderRectilinear
3. Added a new unit test, cont/testing/UnitTestDataSetBuilderRectilinear.cxx
4. Provided additional tests for UnitTestDataSetBuilderRegular.cxx.
The new tests in (4) were also included in (3), and provide a much more robust way of validating datasets created. It has nested for loops to do an all-all test on various ways to specify the X,Y, and Z coordinates.  It computes the bounds on the coordinate system and make sure they are correct.
Note: The GetBounds() call for Rectilinear is not working, and is an item for future discussion. It is disabled for now.
2015-12-30 12:34:04 -05:00
CMake Enable vectorization inside the Serial and TBB backends. 2015-11-25 15:59:13 -05:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Fix conversion warnings in streamline example. 2015-11-30 14:14:07 -07:00
vtkm This commit has several changes. 2015-12-30 12:34:04 -05:00
CMakeLists.txt Merge topic 'enable_vectorization' 2015-11-27 09:36:15 -05:00
CONTRIBUTING.md Add a contributing guide to vtk-m. 2015-07-29 17:33:30 -04:00
CTestConfig.cmake The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -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