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Robert Maynard 3eec5e86df ICC: disable vectorization as both ivdep and simd generate bad code.
We are disabling the entire vectorization hints for ICC as it generates
both bad code, and dramatically decreases compile time.

The compiler does not check for aliasing or dependencies that might cause
incorrect results after vectorization, and it does not protect against illegal
memory references. #pragma ivdep overrides potential dependencies, but the
compiler still performs a dependency analysis, and will not vectorize if it
finds a proven dependency that would affect results. With #pragma simd, the
compiler does no such analysis, and tries to vectorize regardless.
2016-03-23 14:10:18 -04:00
CMake Make the default for vectorization be none. 2016-03-23 14:09:59 -04:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Fix example that was using the old interface to VTKDataSetWriter 2016-03-17 09:13:25 -06:00
vtkm ICC: disable vectorization as both ivdep and simd generate bad code. 2016-03-23 14:10:18 -04:00
CMakeLists.txt Add a CTestCustom file to to filter out warnings that cant be eliminated 2016-03-17 13:14:30 -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

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