CUDA: Difference between revisions

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* NVIDIA Driver
* NVIDIA Driver


{{Note | If you are unsure if your device is CUDA capable, feel free to contact staff@umiacs.umd.edu}}
{{Note | If you are unsure if your device is CUDA capable, please [[HelpDesk |contact staff]].}}


==Getting Started==
==Getting Started==
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#: <pre> module load cuda</pre>
#: <pre> module load cuda</pre>
# Obtain a copy of the cuda samples:
# Obtain a copy of the cuda samples:
#: <pre>rsync -a /opt/common/cuda/$CUDA Version/samples/ ~/cuda_samples</pre>
#: <pre>rsync -a /opt/common/cuda/<CUDA Version>/samples/ ~/cuda_samples</pre>
# Build and run the device query
# Build and run the device query
#: <pre> cd ~/cuda_samples/1_Utilities/deviceQuery/ && make && ./deviceQuery</pre>
#: <pre> cd ~/cuda_samples/1_Utilities/deviceQuery/ && make && ./deviceQuery</pre>


Assuming the deviceQuery complication completed without error, you should now see output listing the details of the GPUs in your system.  If desired you can compile additional samples by switching to their respective directory and running 'make'
Assuming the deviceQuery complication completed without error, you should now see output listing the details of the GPUs in your system.  If desired you can compile additional samples by switching to their respective directory and running 'make'

Latest revision as of 20:39, 26 February 2024

CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

Prerequisites

  • NVIDIA GPU device
  • NVIDIA Driver
Exclamation-point.png If you are unsure if your device is CUDA capable, please contact staff.

Getting Started

  1. Load the CUDA Environmental variables via GNU Modules
    • Multiple versions are available. See the modules documentation and module list cuda for more information.
     module load cuda
  2. Obtain a copy of the cuda samples:
    rsync -a /opt/common/cuda/<CUDA Version>/samples/ ~/cuda_samples
  3. Build and run the device query
     cd ~/cuda_samples/1_Utilities/deviceQuery/ && make && ./deviceQuery

Assuming the deviceQuery complication completed without error, you should now see output listing the details of the GPUs in your system. If desired you can compile additional samples by switching to their respective directory and running 'make'