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Kenneth Moreland 427f3cf57d Make Rectilinear and Regular builder tests faster 
Previously UnitTestDataSetBuilderRectilinear and
UnitTestDataSetBuilderRegular did an exhaustive test of all possible
grid sizes within a certain amount of dimensions and with some number of
ways to build point arrays. This created hundreds of thousands of cases
that were checked, and it was running a long time. At best it wasted
time and at worst ctest reported the test as timed out.

It is not really necessary to perform an exhaustive test. The tests are
changed to do 10 trials using random values for dimensions and fill
methods.
2016-01-14 15:15:19 -07:00
CMake Extend the timeout for vtkm worklet tests to reduce timeout failures. 2015-12-10 15:28:36 -05:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Update all of vtkm to understand it can only identify as one compiler. 2016-01-12 11:05:40 -05:00
vtkm Make Rectilinear and Regular builder tests faster 2016-01-14 15:15:19 -07:00
CMakeLists.txt Teach VTK-m how to specify the CUDA GPU architecture to build for. 2015-12-09 13:17:00 -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