ed43dad6ca
Previously, DynamicArrayHandle and DynamicCellSet had slightly different interfaces to their CastTo feature. It was a bit confusing and not all that easy to use. This change simplifies and unifies them by making each class have a single CopyTo method that takes a reference to a cast object (an ArrayHandle or CellSet, respectively) and fills that object with the data contained if the cast is successfull. This interface gets around having to declare strange types. Each object also has a Cast method that has to have a template parameter specified and returns a reference of that type (if possible). In addition, the old behavior is preserved for DynamicArrayHandle (but not DynamicCellSet). To avoid confusion, the name of that cast method is CastToTypeStorage. However, the method was chaned to not take parameters to make it consistent with the other Cast method. Also, the IsType methods have been modified to reflect changes in cast/copy. IsType now no longer takes arguments. However, an alternate IsSameType does the same thing but does take an argument. |
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| CMake | ||
| docs | ||
| examples | ||
| vtkm | ||
| CMakeLists.txt | ||
| CONTRIBUTING.md | ||
| CTestConfig.cmake | ||
| LICENSE.txt | ||
| 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