Parallel efficiency drops inconsistently

Question

My question is probably of trivial nature. I parallelised a CFD code using MPI libraries and now I am trying to investigate my parallel efficiency. To start with, I created a case which would provide equal loads among the ranks and constant ratio of volume of calculations over transferred data. Thus, my expectation would be that as I increase the ranks, any runtime changes would be attributed to the communication delays only. However, I realised that subroutines that do not invoke rank communication (so they only do domain calculations, hence they deal with the same load for all ranks) contribute significantly-actually the most- runtime increases. What am I missing here? Does this even make sense?

Answer 1

Does this even make sense?

Yes!

The more processes you create (every process has a rank), the more you reach the limit of your system's capability to execute processes in a truly parallel manner.

Your system (e.g. your computer) can run in parallel a certain amount of processes, when this limit is surpassed, then some processes wait to be executed (thus not all processes run in parallel), which harms performance.

For example, assuming that a computer has 4 cores and you create 4 processes, then every core can execute a process, thus your performance is harmed by the communicated between the processes, if any.

Now, in the same computer, you create 8 processes. What will happen?

4 of the processes will start execute in parallel, but the other 4 will wait for a core to get available, so that they can run too. This is not a truly parallel execution (some processes will execute in linear fashion). Moreover, depending on the OS scheduling policy, some processes may be interleaved, causing overhead at every switch.