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Kenneth Moreland c0f49d6112 Update vtkm_configure_device macro
* Support a REQUIRED flag that only gives an error if that flag is given.

* Move common configuration required for all devices (such as boost) to a
special device named Base.

* Make CUDA always capitalized to be consistent with the other CMake
variables.

* Rather than call include_directories, set a variable named
VTKm_INCLUDE_DIRS. This is consistent with how most CMake packages work.

* Make a CMake variable named VTKm_LIBRARIES containing all the
libraries the configured devices need.

* Automatically configure supported devices when loading the VTK-m
package in CMake.
2015-11-12 14:26:00 -07:00
CMake Update vtkm_configure_device macro 2015-11-12 14:26:00 -07:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Merge branch 'scatter-worklets' into 'master' 2015-11-11 13:09:47 -05:00
vtkm Merge branch 'scatter-worklets' into 'master' 2015-11-11 13:09:47 -05:00
CMakeLists.txt Update vtkm_configure_device macro 2015-11-12 14:26:00 -07:00
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

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 cores 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