f9750e83f7
I ran into a few minor issues with the constructors to the Field class. The big change I made was that I removed the Field constructors that take an example type and create an empty field of that type. The problem was that the example type was easily confused with some other type that was supposed to describe an array. This lead to some odd behavior in the compiler and resulted in errors in unexpected places. The use case for this constructor is dubious. There were several tests in the code that would create an empty field, add it to a data set, then get it back out to pass to the worklet. The code is much simpler if you just make an ArrayHandle of the right type and use that in the worklet invoke directly. It is also faster to compile with smaller code because the type is known statically (whereas it is lost the other way). The other change was to declare references to ArrayHandle and DynamicArrayHandle as const. There is nothing in the behavior that invalidates the const, and it accepts arrays constructed in the parameter. |
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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