GPU

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Implementation

In GRASS 7 you can ./configure GRASS with:

 --with-opencl

On Linux you'll need to use the proprietary driver from nVidia, AMD (ATI), or Intel. Point --with-opencl-includes= to the directory above cl.h, and as long as libOpenCL.so is in the ldconfig search path you're ok (a symlink to /usr/local/lib might be needed). On Mac OSX OpenCL support is now built in, and the framework should be automatically detected when you use --with-opencl in the ./configure options.

Currently r.sun is the first module with any OpenCL support (see here, although integration of Seth's work into svn-trunk is not yet complete.

Discussion

Comments from the mailing list concerning GRASS and GPU parallelization:

  • As I understand it, CUDA is 100% dependent on the closed-source binary driver from nVidia and works on their video cards alone. Which is fine for today for people with nVidia hardware using their binary video card driver. If nVidia decides in a couple of years to stop supporting CUDA, your old card, your specific OS or distro, your OS or distro version+cpu type, or if they go out of business or are bought/sold to another company who is not interested, any code based on it becomes useless. For this reason code written for an open platform such as OpenCL, even if less advanced, seems to have a brighter long-term future. -- HB
  • Support for double precision floating point values must be retained for calculations which deal with positional data (as sub-meter precision for lat/long exceeds single-precision floating poing). For elevation and radiometric data floating point precision may be enough.

Further reading

  • Steinbach, M., Hemmerling, R., 2011. Accelerating batch processing of spatial raster analysis using GPU. Computers & Geosciences. DOI
  • See the "Parallelization" category listing at the bottom of this page.

Modules of interest to be parallelized

The target version will be GRASS 7 (alias SVN trunk).

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