Odyssey has a number of nodes that have NVIDIA Tesla general purpose graphics processing units (GPGPU) attached to them. It is possible to use CUDA tools to run computational work on them and in some use cases see very significant speed ups.
One node with 8 Tesla K20Xm is available for general use from the
gpu partition; the remaining are nodes are owned by various research groups available in their private partitions (and may be available when idle through serial_requeue and the options shown below.) Direct access to these nodes by members of other groups is by special request. Please visit the RC Portal and submit a help request for more information.
GPGPU's on SLURM
To request a single GPU on slurm just add
#SBATCH --gres=gpu to your submission script and it will give you access to a GPGPU. To request multiple GPUs add
#SBATCH --gres=gpu:n where 'n' is the number of GPUs. You can use this method to request both CPUs and GPGPUs independently. So if you want 1 CPU and 2 GPGPUs from our general use GPU nodes in the 'gpu' partition, you would specify:
#SBATCH -n 1
The current version of the Nvidia driver installed on all GPU-enabled nodes on the Odyssey cluster is 396.26, which supports Cuda version 9.
To load the toolkit and additional runtime libraries (cublas, cufftw, ...) remember to always load the module for
cuda in your Slurm job script or interactive session.
>$ module load cuda/9.0-fasrc02
Please Note that in the past our Cuda installations were heterogeneous and different nodes on the cluster would provide different versions of the Cuda driver.
For this reason might have used in your job submissions the Slurm flags
--constraint=cuda-$version (for example --constraint=cuda-7.5) to specifically request nodes that were supporting that version.
This is no longer needed as our cuda modules are the same throughout the cluster, and you should remove those flags from your scripts.
Using CUDA-dependent modules
CUDA-dependent applications are accessed on Odyssey in a manner that is similar to compilers and MPI libraries. For these applications, a CUDA module must first be loaded before an application is available. For example, to use cuDNN, a CUDA-based neural network library from NVIDIA, the following command will work:
If you don't load the CUDA module first, the cuDNN module is not available.
$ module load cudnn/7.0_cuda9.0-fasrc01
Lmod has detected the following error:
The following module(s) are unknown: "cudnn/7.0_cuda9.0-fasrc01"
module-queryor our user portal https://portal.rc.fas.harvard.edu/apps/modules to find out versions available and how to load them.
More information on software modules can be found here.
See an example on how use the cuda module to install and use Tensorflow.
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