c5c1dee4fc
Clean up CellSet Underneath the CellSet implementation is a set of supporting classes that manage the actual structure in both the control and execution environments. However, the implementation of these classes was a bit confusing and inconsistent. The following changes are made: * Most significantly, there is no longer any Connectivity classes in the control environment. This functionality has been wrapped up into the CellSet classes, which is more consistent and easier to understand. (There was a definite distinction between CellSet and Connectivity, but it was subtle and difficult to understand.) This also means that edits to CellSets happen to CellSets directly. * The set of classes for structured and explicit cell sets match. There is different functionality within, but the class naming and meaning are consistent. * Make the class names more consistent with the rest of VTK-m class names. Specifically classes like ExplicitConnectivity become ConnectivityExplicit. Also, the words regular and structured were being used interchangeably. Now, always use structured except when dealing specifically with grids of regular spacing. * The connectivity classes were using the nomenclature "From" and "To" to specify topological elements of links. The same concept in worklet classes were using the nomenclature "Src" and "Dest." For consistency, all references are changed to "From" and "To". * Unlike explicit cell sets, structured cell sets have functionality shared between control and execution environments. Rather than duplicate it or create unique exposed classes, have a shared internal implementation in vtkm::internal. See merge request !117 |
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| CMake | ||
| docs | ||
| 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