When you do a “/usr/local/cuda-10.1/extras/demo_suite/deviceQuery”. You might get the errors seemed above
[root@node1 ~]# /usr/local/cuda-10.1/extras/demo_suite/deviceQuery
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 35
-> CUDA driver version is insufficient for CUDA runtime version
Result = FAIL
The Issue may cause some confusion. It is not your libraries. But the it is the Power Setting at the BIOS. Most Servers are configured to be balanced. But for GPGPU, you need to put Power to “Maximum Performance”. For example, for HPE Server, you should put “Static High Performance Mode”
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To test whether you have compiled your GROMACS correctly with the CUDA drivers and runtime. You can use the command
% gmx_mpi --version
You should see
GPU support: CUDA
CUDA driver: 10.10
CUDA runtime: 10.10
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How to install CUDA Python followed by a tutorial on how to run a Python example on a GPU
Nvidia and IBM did a complex proof-of-concept to demonstrate the scaling of AI workload using Nvidia DGX, Red Hat OpenShift and IBM Spectrum Scale at the example of ResNet-50 and the segmentation of images using the Audi A2D2 dataset. The project team published an IBM Redpaper with all the technical details and will present the key learnings and results.