Shuenhoy/vtk-m2
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Kenneth Moreland 55c159d6f0 Check error codes from CUDA functions 
Most functions in the CUDA runtime API return an error code that must be
checked to determine whether the operation completed successfully. Most
operations in VTK-m just called the function and assumed it completed
correctly, which could lead to further errors. This change wraps most
CUDA calls in a VTKM_CUDA_CALL macro that checks the error code and
throws an exception if the call fails.
2016-12-14 10:43:44 -07:00
CMake make sure cuda test build executables have all include directories. 2016-11-30 17:12:41 -05:00
docs Remove exports for header-only functions/methods 2016-11-15 22:22:13 -07:00
examples Remove exports for header-only functions/methods 2016-11-15 22:22:13 -07:00
vtkm Check error codes from CUDA functions 2016-12-14 10:43:44 -07:00
CMakeLists.txt Remove boost CMake logic as VTK-m doesn't require boost now. 2016-10-21 08:41:22 -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 Update the documentation to reflect we don't require boost. 2016-10-21 08:41:22 -04:00
README.md Update the documentation to reflect we don't require boost. 2016-10-21 08:41:22 -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 required dependencies are:

VTK-m optional 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