Johann:

Could it be that your CPU has a single floating-point unit shared by 4
cores on a single die, and thus only 2 floating-point units total for
all 8 of your cores?  If so, then that fact, plus the fact that each
core has its own separate ALU for integer operations, would seem to
explain the results you are seeing.  I see similar results on my 2-
core Intel Core 2 Duo when running your test.

I tried to check out that possibility by doing some searches on the
Internet about the Xeon, but couldn't quickly find a definitive
answer.  I'm hoping someone else on the group might know the answer
off the top of their head.

Andy


On Aug 4, 1:59 am, Johann Kraus <johann.kr...@gmail.com> wrote:
> > My guess would be you're seeing the overhead for pmap since the
> > (inc 0.1) computation is so cheap.  From the docs for pmap:
> >   "Only useful for computationally intensive functions where the time of
> >   f dominates the coordination overhead."
>
> I don't think so, as the cheap computation (inc 0.1) is packed into
> the long running (dotimes). And it does work for using (inc 0), where
> I get a perfect linear speedup. Just by using (inc 0.1) instead of
> (inc 0) the speedup is rapidly getting down while the CPU load is next
> to the limit.
>
> > Maybe try this with something more expensive to compute?  Also, making
> > the result a function of the parameter can't hurt since there's a chance
> > the JVM may optimize away the inc of a constant as it stands.
>
> I also would expect the JVM to optimize away something, but for me it
> seems it does not, because again in case of (inc 0) everything works
> perfect.
>
> The point is:
> Why do I get a linear speedup with (inc 0) -> 8 times and not with
> (inc 0.1) -> 2 times, while the CPU load is 800% and 700%,
> respectively?
> __
> Johann

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