Shuenhoy/vtk-m
1
0
Fork 0
mirror of https://gitlab.kitware.com/vtk/vtk-m synced 2024-09-19 02:25:42 +00:00
Code Issues 2 Actions Packages Projects Releases Wiki Activity
767 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%
cf6af174eb
Go to file
Kenneth Moreland cf6af174eb Merge branch 'variable-topology-fields' into 'master' 
Variable topology fields

Changes to fetching in topology maps that lets you properly deal with cases where you do not know how many values are being fetched at compile time. For example, explicit cell sets can have any number of cell shapes that have different numbers of nodes.

This change should resolve issue #26.

See merge request !128
2015-08-19 11:27:43 -04:00
CMake Update FindBoostHeaders to properly detect a minimum Boost version. 2015-08-17 10:06:57 -04:00
docs The Copyright statement now has all the periods in the correct location. 2015-05-21 10:30:11 -04:00
vtkm Merge branch 'variable-topology-fields' into 'master' 2015-08-19 11:27:43 -04:00
CMakeLists.txt Update VTK-m to error out nicely when Boost isn't found. 2015-08-17 10:07:17 -04:00
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 Add TBB backend. 2015-06-01 13:57:37 -04:00
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