Shuenhoy/vtk-m
1
0
Fork 0
mirror of https://gitlab.kitware.com/vtk/vtk-m synced 2024-09-19 10:35:42 +00:00
Code Issues 2 Actions Packages Projects Releases Wiki Activity
1,370 Commits 9 Branches 24 Tags 38 MiB
C++ 95.1%
CMake 3.2%
Cuda 0.5%
Python 0.4%
Shell 0.3%
Other 0.5%
e874b52f18
Go to file
Kenneth Moreland e874b52f18 Support control array portals in composite vector. 
Previously, the ArrayHandleCompositeVector had a separate implementation
of ArrayPortal for the control and execution environments. Because I was
lazy when I implemented it, the control version did not support Get.

Since originally implementing this class, VTK-m now allows defining
methods that are declared as working in both control and execution
environments (VTKM_EXEC_CONT_EXPORT) but only work in one or the other
depending on methods of templated subclasses they call. Thus, solve this
problem by simply removing the control version of the portal and use the
same portal for both.
2016-01-04 12:53:56 -07:00
CMake Extend the timeout for vtkm worklet tests to reduce timeout failures. 2015-12-10 15:28:36 -05:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
examples Extend vtkm::DeviceAdapterTraits to include a unique numeric identifier. 2015-12-16 11:18:52 -05:00
vtkm Support control array portals in composite vector. 2016-01-04 12:53:56 -07:00
CMakeLists.txt Teach VTK-m how to specify the CUDA GPU architecture to build for. 2015-12-09 13:17:00 -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