🚀 go-pugleaf

From article <10164@encore.Encore.COM>, by jdarcy@multimax.UUCP (Jeff d'Arcy):
> From article <7651@bunny.GTE.COM>, by hhd0@GTE.COM (Horace Dediu):
>> Who cares about shared memory?  Distributed is the only way to scale.
> 
> I'd like to see you back this one up with some *real* proof.  I could
> possibly agree with the statement that distributed is the *best* way
> to scale, or that distribution is necessary for *large* (>~30) scale
> multiprocessing.  I think that shared memory architectures will still
> be viable for a long time, perhaps as a component of a distributed
> environment.  If you disagree please provide reaasons.

Distributed architectures are obviously most desirable from a hardware point of
view, because they are simple and (nearly) arbitrarily scalable.  IMHO there
are two reasons why shared memory architectures will continue to be more
important for many of us for a long time to come.

1) Shared memory systems are *much* easier to program, and software development
costs *much* more than hardware nowadays.  I say this based on my experience as
a hardware engineer in a mostly software engineering environment.

2) Many problems have proven inefficient so far on distributed memory
architectures.  Distributed machines succeed beautifully on problems where some
real physical space can be mapped onto processor/memory nodes which are
arranged in a regular topology which is similar to the topology of the problem.
Modeling physical media, such as solids, liquids or gases, takes advantage of
the fact that the state of one parcel only directly affects the state of its
nearby neighbors.

Problems with irregular topology, such as electronic circuits, are very much
harder to solve efficiently, because the state of one gate or transistor may
affect the state of another at a great distance. Communications is far more
irregular and expensive, so speedups suffer.  Static load balancing among the
processors is also much harder.  Shared architectures need not statically
partition the problem, and communication speed depends much less on distance.

I'm aware of new algorithmic technology being developed to attack these
problems, such as distributed time discrete event simulation techniques, but 
there's still a lot of work ahead.

I agree that distributed memory is the only way to scale if you can, but there
are important problems which are much more readily solved on shared
architectures, at least so far.  A hybrid architecture, with physically
distributed but logically shared memory, of which several examples have been
built, may be the best transition path.
-- 
Michael Butts, Research Engineer       KC7IT           503-626-1302
Mentor Graphics Corp., 8500 SW Creekside Place, Beaverton, OR 97005
!{sequent,tessi,apollo}!mntgfx!mbutts         mbutts@pdx.MENTOR.COM
Opinions are my own, not necessarily those of Mentor Graphics Corp.