I have a submit script looks like below, it tries to run a large number of instances of csce.py in backgrounds with 3 nodes.... in a laptop, this usually could successfully automatically distribute all the background tasks into 16 cores.... However, I am not sure if in a cluster, it would also automatically distribute the 4*13*9 tasks in 3 nodes (48 cores).#!/bin/bash#SBATCH -N 3                   # Total number of nodes requested (16 cores/node)#SBATCH -n 48                  # Total number of mpi tasks requestedfor simplify in  0.1 0.15 0.2 0.25do for lmbda in 0.5 1 2  5 10 20  50 100 200 500 1000 2000 5000do for mu in 0.005 0.01 0.05 0.1 0.5 1 5 10 50 do rm eci.outcsce.py --mu $mu --lmbda $lmbda --simplify $simplify --favor-low-energy 0.01 --bias-stable --save-energies lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_ce-energies.dat --save-weights lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_ce-weights.dat  --casm-eci-file eci.in lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_eci.out --save-hull lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_ce-hull.dat  --preserve-ground-state 10000 2> lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_error 1> lmbda_$lmbda\_mu_$mu\_simplify_$simplify\_output &done  done donewait
submitting jobs to get 3 nodes running parallel executions
parallelism;batch jobs
No, if you would have multiple nodes (machines) there is nothing in there that takes advantages of that, everything will run on the machine you run this script.The & at the end of the csce.py line just makes the operation run in the background on the current machine. So wit this setup you will get 4x12x9 tasks running in parallel on your current machine.GNU parallel supports remote execution, for that you need to setup automated access to the other machines and think about how any input data is accessed (if it is not stored for reading on some volume shared by all machines you might need to copy the data to work on).