🚀 go-pugleaf

In article <MCCALPIN.89Oct18103933@masig3.ocean.fsu.edu> mccalpin@masig3.ocean.fsu.edu (John D. McCalpin) writes:
>In article <35896@lll-winken.LLNL.GOV> brooks@maddog.llnl.gov (Eugene
>Brooks) writes:
>
>>Supercomputers of the future will be scalable multiprocessors made of
>>many hundreds to thousands of commodity microprocessors.

>I think that it is interesting that you expect the same users who
>can't vectorize their codes on the current vector machines to be able
>to figure out how to parallelize them on these scalable MIMD boxes.
>It seems to me that the automatic parallelization problem is much
>worse than the automatic vectorization problem, ...

Yes, there seems to be the perception running around that
"parallelization" must be harder than "vectorization".  I am not
saying it isn't, because I and not a compiler writer, but I sure can
give some reasons why it might not be.

Vectorization requires the same operation to be repeatedly performed
on the elements of a vector.  Parallel processors can perform
different operations, such as conditional branching within a loop that
is being performed in parallel.

Dependencies between loop iterations can be handled in a PP that has
the appropriate communication capabilities, whereas most (all?)
vector machines require that all elements be independent (except for
certain special cases, like summation and dot product.)  This can be
done by message passing, or if you have shared memory, with
interlocks.

Parallel processors are not limited to operations for which there are
corresponding vector instructions provided in the hardware.

Well that's all I can think of right now.  Anyone else care to add
anything?
-Stan