best parallelisation strategy on python
I have a code fortran 90 that is parallelised with MPI. I would like to traslate it in python, but I am not sure on the parallelisation strategy and libraries. I work on clusters, with each node with 5GB memory and 12 processors or 24 processors (depending on the cluster I am using). Ideally I would like to split the computation on several nodes.
Let me explain what this code does: It read ~100GB data, they are divided in hdf5 files of ~25GB each. The code should read the data, go through it and then select a fraction of the data, ~1GB and then some CPU intensive work on it, and repeat this process many times, say 1000 times, then write the results to a single final file.
I was thinking that the CPU intensive part would be written as a shared object in C.
Do you have suggestions about which library to use?