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Thomas J. Otahal 556b922733 Created a filter for the vector magnitude worklet. 
Modified the vector magnitude worklet to accept VecAll instead
of Vec3 and return Scalar. Modified the Magnitude() and Sqrt() functions
to return FloatDefault for all inputs except for Float64.

Perhaps we should modify other functions in Math.h and VectorAnalysis.h to
return float types for intergral arguments instead of integral types?
2017-01-19 13:27:42 -07:00
CMake make sure cuda test build executables have all include directories. 2016-11-30 17:12:41 -05:00
docs Remove exports for header-only functions/methods 2016-11-15 22:22:13 -07:00
examples Remove exports for header-only functions/methods 2016-11-15 22:22:13 -07:00
vtkm Created a filter for the vector magnitude worklet. 2017-01-19 13:27:42 -07:00
CMakeLists.txt Remove boost CMake logic as VTK-m doesn't require boost now. 2016-10-21 08:41:22 -04:00
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 Update the documentation to reflect we don't require boost. 2016-10-21 08:41:22 -04:00
README.md Update the documentation to reflect we don't require boost. 2016-10-21 08:41:22 -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 required dependencies are:

VTK-m optional 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