20c1a04894
ArrayHandles in DAX have a CopyInto function which allows the user to copy an array handle's data into a compatible STL type iterator. Originally this was fairly straight forward to implement since array handles in DAX are templated on the DeviceAdapterTag. In contrast, VTKm array handles use a polymorphic ArrayHandleExecutionManager under the hood allowing a single array handle to interface with multiple devices at runtime. To achieve this virtual functions are used. This makes implementing the CopyInto function difficult since it is templated on the IteratorType and virtual functions cannot be templated. To work around this, I've implemented a concrete templated CopyInto function in the class derived from ArrayHandleExecutionManagerBase. In the ArrayHandle class, CopyInto dynamically casts the base class into the derived class, then calls the CopyInto function defined in the derived class. The drawback to this approach is that, should the user define their own class that inherits from ArrayHandleExectionManagerBase, they are not forced to implement the CopyInto function, unlike the other virtual functions. |
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| docs | ||
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| 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