Difference between revisions of "Applications/Cuda"
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* 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). | * 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: 6.5.14, 7.5.18, 8.0.61 | + | * Version: 6.5.14, 7.5.18, 8.0.61, 9.0.176 and 10.1.168 ('''preferred''') |
− | * Modules: cuda/6.5.14, cuda/7.5.18, cuda/8.0.61 | + | * Modules: cuda/6.5.14, cuda/7.5.18, cuda/8.0.61, cuda/9.0.176 and cuda/10.1.168 |
* Licence: Free, but owned by NVidia | * Licence: Free, but owned by NVidia | ||
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|style="width:5%; border-width: 0" | [[File:icon_tick.png]] | |style="width:5%; border-width: 0" | [[File:icon_tick.png]] | ||
− | |style="width:95%; border-width: 0" | NVIDIA CUDA® modules | + | |style="width:95%; border-width: 0" | 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. |
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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/ | + | [username@gpu03 ~]$ module add cuda/10.1.168 |
[username@gpu03 ~]$ ./gpuTEST | [username@gpu03 ~]$ ./gpuTEST | ||
Revision as of 08:28, 30 March 2020
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: 6.5.14, 7.5.18, 8.0.61, 9.0.176 and 10.1.168 (preferred)
- Modules: cuda/6.5.14, cuda/7.5.18, cuda/8.0.61, cuda/9.0.176 and cuda/10.1.168
- Licence: Free, but owned by NVidia
Usage Examples
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/10.1.168 [username@gpu03 ~]$ ./gpuTEST
Further Information