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Kenneth Moreland 92d2ad6a72 Consolidate dynamic_cast calls. 
Previously in DynamicArrayHandle the dynamic_cast was contained in a
method that was reimplemented for every different instance of
DynamicArrayHandleBase. Change that to put the dynamic_cast in a
function outside of the class so that there is only one implementation
created per ArrayHandle type.

Similarly, DynamicCellSet had its dynamic_cast in a method plus there
was a second version in the functors used for the CastAndCall method.
Consolidate all of these into a function outside of either much like
DynamicArrayHandle.
2016-01-18 15:55:02 -07:00
CMake Extend the timeout for vtkm worklet tests to reduce timeout failures. 2015-12-10 15:28:36 -05:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Use the DataSetBuilderExplicitIterative helper where it is useful. 2016-01-18 16:19:48 -05:00
vtkm Consolidate dynamic_cast calls. 2016-01-18 15:55:02 -07:00
CMakeLists.txt Teach VTK-m how to specify the CUDA GPU architecture to build for. 2015-12-09 13:17:00 -05:00
CONTRIBUTING.md Add a contributing guide to vtk-m. 2015-07-29 17:33:30 -04:00
CTestConfig.cmake The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
LICENSE.txt Fix compile time errors 2015-08-21 11:17:10 -07:00
README.md Update ReadMe to reference gitlab. 2015-05-13 08:45:52 -04:00

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:

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