I'd say they're not fundamentally at odds, but they're at odds with a "greedy approach". That is, it is much easier to scale out when you're willing to make con

I'd say they're not fundamentally at odds, but they're at odds with a "greedy approach". That is, it is much easier to scale out when you're willing to make constraining assumptions about your program; and willing to pay a lot of overhead for distributed resource management, migrating pieces of work etc. If you want to scale while maintaining efficiency, you have to be aware of more things about the work that's being distributed; you have to struggle much harder to avoid different kinds of overhead and idleness; and if you really want to go the extra mile you need to think of how to turn the distribution partially to your _benefit_ (example: Using the overhead you pay for fault-tolerance or high-availability by storing copies of your data in different formats, allowing different computations to prefer one format over the other; while on a single machine you wouldn't even have the extra copies).