Yes, for example when I did my PhD work ~2001 with a T3E I could run a simulation of a duplex DNA in a box of water by running it in parallel. This was true both for memory and CPU reasons. It limited to me studying a single sequence at a time, or 2-3 which was the practical limit on the number of concurrent jobs. This used the well-balanced design of the T3E, which had a great MPI system.
Eventually it reached the point (~2007) where I could fit the whole simulation on a single 4-core Intel box with similar performance. Then, I ran one "task" per machine, and scaled to the number of available machines. This uses
only inter-node communication, which goes over a hub or crossbar on the motherboard. Much faster.
Now, I can fit many copies of DNA on a single machine (one task per core). This is far and away the best, because each processor just accesses its own memory, greatly reducing motherboard traffic, so the problem is basically CPU-bound instead of communication bound (this also now applies to GPUs, such that single GPUs can run one large simulation within its own RAM and not have to spill data back and forth over the CPU/GPU communication path).
This moves the challenge to the IO subsystem- I generate so much simulation data that I need a fat MapReduce cluster to analyze the trajectories.
none of this is news - what you're describing is really just strong scaling. and sure, most systems already have subsets of nodes set aside for post-simulation cleanup.
I'm not just describing strong scaling. I'm describing a cost-effective way to achieve it; that's what really matters.
Why have subsets of nodes for post-simulation cleanup? Why not just run that cleanup on the same nodes you used for simulation? Or other general nodes? Otherwise, you've got two sets of nodes which are used at lower utilization than they would normally be.
Eventually it reached the point (~2007) where I could fit the whole simulation on a single 4-core Intel box with similar performance. Then, I ran one "task" per machine, and scaled to the number of available machines. This uses only inter-node communication, which goes over a hub or crossbar on the motherboard. Much faster.
Now, I can fit many copies of DNA on a single machine (one task per core). This is far and away the best, because each processor just accesses its own memory, greatly reducing motherboard traffic, so the problem is basically CPU-bound instead of communication bound (this also now applies to GPUs, such that single GPUs can run one large simulation within its own RAM and not have to spill data back and forth over the CPU/GPU communication path).
This moves the challenge to the IO subsystem- I generate so much simulation data that I need a fat MapReduce cluster to analyze the trajectories.