Please make sure you work in GLOBAL $HOME and not $SCRACH
On the login node (zohim@login19:~>) in Perlmutter, run the following commands:
- Pull the latest image:
shifterimg pull nvcr.io/nvidia/nightly/cuda-quantum:latest
new container name:
shifterimg pull nvcr.io/nvidia/nightly/cuda-quantum:cu12-latest
- Enter the image to add some configuration:
shifter --image=docker:nvcr.io/nvidia/nightly/cuda-quantum:latest --module=cuda-mpich /bin/bash
- Copy over the distributed_interfaces folder:
cp -r /opt/nvidia/cudaq/distributed_interfaces/ .
3.5. Pip install any packages you would like
- Exit the image:
exit
- Activate the native MPI plguin
export MPI_PATH=/opt/cray/pe/mpich/8.1.27/ofi/gnu/9.1
source distributed_interfaces/activate_custom_mpi.sh
Make sure the distributed_interfaces folder from step 5 above is in home directory.
- Verify the successful creation of the local library and environment variable:
echo $CUDAQ_MPI_COMM_LIB
- Shifter into the container again and copy some files:
shifter --image=docker:nvcr.io/nvidia/nightly/cuda-quantum:latest --module=cuda-mpich /bin/bash
cp /usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.11.8.89 ~/libcudart.so
exit
Now you have all the settings required to run CUDA-Q on multiple nodes on Perlmutter.
Create a .py file you would like to execute and a jobscript.sh file. See examples of these in the repo.
Execute the job via sbatch jobscript.sh on terminal.
The output will be a slurm-jobid.out file, an example of which is also in the repo.