a5972e6a15
f86382f0 Fix support for CoordinateSystems using ArrayHandleCartesianProduct. d6a2a142 Add toleranced compare for values. Add tests for vtkm::Float32,Float64,Id typed arrays. 5d438353 Add toleranced comparisions for bounds validation. Also, add vtkm::Float32 and vtkm::Float64 to the testing for rectilinear and regular datasets. b225ae97 Rectilinear coordinates (created with DataSetBuilderRectilinear) are now converted to vtkm::FloatDefault. This reduces the number of types to consider when casting inside CoordinateSystem, and was felt by all to be a reasonable restriction. d755e43d Use ArrayHandleCompositeVector to represent separated point arrays for DataSetBuilderExplicit.h. c7b0ffb8 Add tests for DataSetBuilderExplicit. Added cont/testing/ExplicitTestData.h which includes several explicit datasets. These datasets come from VTK data generated in VisIt. The new unit tests build datasets in several different ways and do some basic validation. b4d04fff Add specialization of printSummary_ArrayHandle for UInt8. It prints them as characters, which are a little hard to understand to this computer scientist. bd929c20 Fix compiler warnings. ... Acked-by: Kitware Robot <kwrobot@kitware.com> Acked-by: Kenneth Moreland <kmorel@sandia.gov> Merge-request: !262 |
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vtkm | ||
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CONTRIBUTING.md | ||
CTestConfig.cmake | ||
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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:
- CMake 3.0
- Boost 1.52.0 or greater
- Cuda Toolkit 6+ or Thrust 1.7+
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