Shuenhoy/vtk-m2
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Mark Kim 387d2a6a76 Stop copying the data in the render call. Instead, upload it on the first 
call and then never upload again.

RenderTest: overload the Render function to pass in a Mapper.
UnitTestMapperGLFW: global variable for the MapperGL to pass into RenderTest::Render

MapperGL: Creating the VBO/VAO is now wrapped in check to see if the data's
already been loaded.

Moved some variables to class scope.
2016-08-29 04:33:14 -06:00
CMake Need to add GLFW to OpenGL interp macro to have the location of the glfw3.h 2016-08-12 18:29:59 -06:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples use the matricies from the camera for the VBO rendering. 2016-08-02 13:42:47 -04:00
vtkm Stop copying the data in the render call. Instead, upload it on the first 2016-08-29 04:33:14 -06:00
CMakeLists.txt Add GLFW unittest. 2016-06-30 15:12:53 -04:00
CONTRIBUTING.md Add a contributing guide to vtk-m. 2015-07-29 17:33:30 -04:00
CTestConfig.cmake Switch over to uploading by https as that is required by cdash. 2016-02-23 14:03:52 -05:00
CTestCustom.cmake.in Lossen the CTestCustom regexes 2016-03-18 13:46:31 -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