Yes, your system may run well on 8 cores, but it will still run less than 8 times as fast as on 1 core, or less that 4 times as fast as on 2 cores. The difference is what we call "parallel overhead", and is pretty much unavoidable for problems that are not embarrassingly parallel
, which includes MD. I don't have a good reference discussing the basics of parallel computing, but the wikipedia article on Amdahl's law
should give some clue. It you're interested in a few of the simulations finishing in as little wall clock time as possible, then yes, run on 8 (or more) cores. However, if you're interested in all 100
simulations finishing as soon as possible, then you will be slightly faster with less parallelization (provided you're still keeping the whole cluster busy).
All of this is assuming you don't need to share the cluster with anyone else. If you do, then the human factor comes into play. Submitting many long-running calculations makes for less ideal scheduling than a few short-running ones with higher parallelism, so your colleagues might start complaining.
Our group's most important project is force field development, which involves running many small simulations for a short time (as opposed to a few big ones for a long time), so we routinely take advantage of the notion that less parallelism makes for more efficient CPU usage, even in the design of our computer clusters. Nevertheless, we do have standing recommendations to use minimum numbers of cores for certain kinds of jobs on certain cluster, in order to decrease the incidence of people filling the cluster with jobs that run forever and other people getting impatient. Avoiding this is very much worth a few % in computing efficiency.