[t.teschner]

Lets assume I have a code which shows a good scalability when I run it on a cluster with 32 cores, all in one node.
Now I move to a different cluster which only offers me 16 cores per node, so I schedule a job with 16 cores on 2 nodes. Or, if 8 cores is maximum, I select 4 nodes etc.

I worked with a code in the past that crashed when I was using several nodes because it assumes that all cores would have access to the same memory space, now that I am on two different nodes, I have two different memory spaces and hence the crash. The only workaround was to copy the data of every timestep back to the home directory, assemble it so that every processor could read the data they needed but that was slowing the code down dramatically.

So how do I get data at the inter processor boundaries from the neighbour processor then? What is the best-practice approach in that case? And does the usage of multiple nodes affect the scalability compared to a single node job (assuming the the cores are equal, like in the example above)?