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Kenneth Moreland 2ac8456b5e Add WholeArray* ControlSignature tags 
The WholeArrayIn, WholeArrayInOut, and WholeArrayOut ControlSignature
tags behave similarly to using an ExecObject tag with an
ExecutionWholeArray or ExecutionWholeArrayConst object. However, the
WholeArray* tags can simplify some implementations in two ways. First,
it allows you to specify more precisely what data is passed in. You have
to pass in an ArrayHandle or else an error will occur (as opposed to be
able to pass in any type of execution object). Second, this allows you
to easily pass in arrays stored in DynamicArrayHandle objects. The
Invoke mechanism will automatically find the appropriate static class.
This cannot be done easily with ExecutionWholeArray.
2015-12-07 09:52:29 -07:00
CMake Suppress failure to vectorize warnings from the Intel Compiler. 2015-12-04 09:18:54 -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 vtkm::Float32 with OpenGL, not vtkm::FloatDefault. 2015-12-03 15:02:41 -05:00
vtkm Add WholeArray* ControlSignature tags 2015-12-07 09:52:29 -07:00
CMakeLists.txt Simplify the design of vectorization support. 2015-12-01 11:37:41 -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