well. it's not entirely true, if distributed systems were the future, we would never see multi-core systems at all, there are still tasks that are much easier to do if you have them done on a single shared-memory system then distributed. I don't think they're necesarilly less complex, they just don't deal with any sort of "big data". pypy's translation toolchain is one of those problems, but I can see a lot of situations where a large mostly-read-only set is necessary to be accessible all the time.
regarding "Python is still a natural choice" - don't confuse python as a language and python (CPython) as interpreter. You would not use CPython for any performance-critical tasks probably, at least not without spending significant time rewriting pieces to C, but PyPy is quite usable in some scenarios and the list is only to grow.
> there are still tasks that are much easier to do if you have them done on a single shared-memory system then distributed.
Such as...? A lot of the big users of cray-type systems were for scientific uses. AFAIK a lot of them are seriously looking at cloud or commodity-type clusters as the problems get bigger.
Anyway I am curious if PyPy is aiming at some specific problem that I don't know about. For "web stuff", I think what is proposed is perhaps overly complicated.
If you want a large read-only set of data to fit on one machine and need high performance, Java, Python and the like aren't great choices because they don't give you much control over the memory layout. Python is probably better because you could write a C extension. But PyPy itself is not optimized for memory size (in fact I think it uses more memory to get speed).
regarding "Python is still a natural choice" - don't confuse python as a language and python (CPython) as interpreter. You would not use CPython for any performance-critical tasks probably, at least not without spending significant time rewriting pieces to C, but PyPy is quite usable in some scenarios and the list is only to grow.