821096cfd7
As part of the work to reduce the number of copies of array handles the CUDA backend was broken. The transportation of stack allocated classes to CUDA relies on all member variables being value based, not references/pointers. This correct the issue of sending references to host side memory to CUDA, at the cost of two copies of the Invocation object. When we move to C++11 we need to revisit this work and see if std::move can help reduce the cost of these copies. |
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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