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h1. GPU Productivity Environment

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GPU productivity environments have recently become an important part of scientific computing and visualization. The following is an attempt to define which ones are relevant for the LinkSCEEM-2 community and set an action plan.

BC Policy: LS2_11-99
Date of Policy: 1st January 2012
First Update: 1st December 2011
Second Update: 2nd December 2011
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The main objective of this policy is to provide the following common GPU productivity environment across all LinkSCEEM-2 resources:

|| Package/Tool || Requested version(s) || Compliance level || Reference ||
| CUDA Toolkit | v4.0 | MUST | http://developer.nvidia.com/cuda-toolkit-40 |
| pyCUDA | 2011.2.1 | MUST | http://mathema.tician.de/software/pycuda |
| pyOpenCL | 2011.2 | MUST | http://mathema.tician.de/software/pyopencl |
| MAGMA | v1.1 | MUST | http://icl.cs.utk.edu/magma/ |
| ViennaCL | 1.2.0 | MUST | http://viennacl.sourceforge.net/ |
| AMD math library for OpenCL (APPML) | v1.4 | MUST | http://developer.amd.com/libraries/appmathlibs/Pages/default.aspx |
| Intel OpenCL SDK | v1.5 | SHOULD | http://software.intel.com/en-us/articles/vcsource-tools-opencl-sdk/ |
| CULA | r12 | SHOULD | http://www.culatools.com/ |
| gputools R package | v0.26 | SHOULD | http://cran.r-project.org/web/packages/gputools/index.html |
| rCUDA | v3.1 | SHOULD | http://www.rCUDA.net |

These GPU productivity environment will be supplemented with other such productivity tools as they become available on allocated systems.