Difference between revisions of "Applications/Cuda"
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==Application Details== | ==Application Details== | ||
| − | * Description: CUDA is NVIDIA’s parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU ( | + | * Description: CUDA is NVIDIA’s parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU (Graphics Processing Unit). |
| − | * Version: 9.0.176,10.1.168 and 11.0 | + | * Version: 9.0.176,10.1.168 and 11.5.0 ('''preferred''') |
| − | * Modules: cuda/8.0.61, cuda/9.0.176, and cuda/10.1.168 and cuda/11.0 | + | * Modules: cuda/8.0.61, cuda/9.0.176, and cuda/10.1.168 and cuda/11.5.0 |
* Licence: Free to download, but owned by NVidia | * Licence: Free to download, but owned by NVidia | ||
| − | '''Note''' : Versions 6.5.14 and 7.5.18 are now retired, version 8.0.61 is designated for retirement | + | '''Note''' : Versions 6.5.14 and 7.5.18 are now retired, version 8.0.61 and 11.0.3 (superseded) is designated for retirement |
==Usage Examples== | ==Usage Examples== | ||
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Last login: Fri Mar 16 10:05:54 2018 from gpu03 | Last login: Fri Mar 16 10:05:54 2018 from gpu03 | ||
| − | [username@gpu03 ~]$ module add cuda/11.0 | + | [username@gpu03 ~]$ module add cuda/11.5.0 |
[username@gpu03 ~]$ ./gpuTEST | [username@gpu03 ~]$ ./gpuTEST | ||
Revision as of 13:41, 26 October 2021
Application Details
- Description: CUDA is NVIDIA’s parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU (Graphics Processing Unit).
- Version: 9.0.176,10.1.168 and 11.5.0 (preferred)
- Modules: cuda/8.0.61, cuda/9.0.176, and cuda/10.1.168 and cuda/11.5.0
- Licence: Free to download, but owned by NVidia
Note : Versions 6.5.14 and 7.5.18 are now retired, version 8.0.61 and 11.0.3 (superseded) is designated for retirement
Usage Examples
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All NVIDIA CUDA® modules have the Deep Neural Network library (cuDNN) included. It is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK. |
Note: this example is done on a node with a GPU accelerator, usually access would be achieved with the scheduler
[username@login01 ~]$ interactive -pgpu salloc: Granted job allocation 1014031 Job ID 1014031 connecting to gpu03, please wait... Last login: Fri Mar 16 10:05:54 2018 from gpu03 [username@gpu03 ~]$ module add cuda/11.5.0 [username@gpu03 ~]$ ./gpuTEST
Further Information