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Kenneth Moreland b20c4fc8ef Fix test that fails during optimization. 
After the most recent changes, I noticed that the matrix unit test
started failing for some optimized compiles. I'm not sure if it was
these changes or others. What I think happened is that it was a check
for a Matrix operation that should be invalid. It checked the valid flag
without checking the data (which, of course, is invalid). However, I
think the optimizer saw that the data generated was never used so
removed that part of the computation so the invalid flag was never set.
Add a condition that uses the result even though it should never be
called to hopefully force the compiler to compute it.
2016-04-21 11:02:45 -06:00
CMake Add missing copyright headers. 2016-03-28 11:16:35 -04:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Rename the opengl folder / namespace to interop. 2016-04-13 15:52:15 -04:00
vtkm Fix test that fails during optimization. 2016-04-21 11:02:45 -06:00
CMakeLists.txt Move mesa package to right place. 2016-03-28 08:51:36 -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