This is interesting.

I don't see it the same way, in a couple of regards.

I entirely understand that pertinent times for runs are as you describe. Now we enter the realm of speculation: if SystemModeler, or a future version of it, supports parallelism, then we can imagine a factor of eight speed-up (or a thousand, if running on a cluster or next-generation CPUs). Factor-of-eight takes us from all-weekend to long-day, or long-day to while-I'm-in-class. Factor-of-eight has the potential to be meaningful.

Where did the "... on a desktop" part enter? If SystemModeler is a winner, surely it'll show up on supercomputers, too. Mathematica itself runs on heavy iron in at least a few installations.

More speculation: this is not just about MPI. I don't know whether you use "MPI" as a synecdoche for all parallelism methods; I can imagine, though, that SystemModeler (eventually) learns impressively clever approaches to distribution that allow interesting problems to be spread over hundreds of cores. Algorithmic research of that sort is exactly the kind now being done.

Finally, I'm far less certain that "[t]he things ... worth modeling ... cannot be modeled ... on a desktop." Maybe it's so; perhaps we'll only know by actually doing the science, and seeing in retrospect how it worked. What I see is that plenty of good and meaningful quantum calculations (for example) have been done over the last century, using a range of computational techniques from pencil-and-paper to top-ten supercomputer. I'm personally skeptical that the only significant modelling left to do now is all at the high end of the hardware range. On another hand, I don't see a quick way to falsify either your claim or mine.

I suspect biologists will feel miffed that you left them out of your list. They certainly have demonstrated an ability to chew up cycles on genomics, molecular modelling, enzymology, ...

And we're having this conversation in /r/Economics; what does that mean?