>>>> Parallelization *cannot* provide super-linear speed-up.
>>...
>> The result follows from a simulation argument. Suppose that you had a
>> parallel process that performed better than N times a serial program.
>> Construct a new serial program that simulates the parallel process. There is
>> a contradiction.
>> ...

The paper suggests the cause of the super-linear speed-up is that a
thread gets caught in local optima (which it doesn't escape from within
the designated thinking time). It also says if each thread was given
more time then the speed-up factor may become less.

It sounds like it is definitely worth exploring the parameters of root
parallelization some more.

> At the risk of belaboring the obvious, extra memory associated with
> each processor or node (cache, main memory, hard disk, whatever) is
> one reason adding nodes can in practice give a speedup greater than
> the increase in processing power.

That is an interesting idea. The paper says: "experiments were performed
on the supercomputer Hyugens, which has 120 nodes, each with 16 cores
POWER5 running at 1.9Ghz and having 64Gb of memory per node."

Experiments were done from 1 to 16 threads, but I cannot see mention in
the paper whether 16 threads means one nodes using 16 cores, or 16 nodes
using one core of each. Perhaps Guillaume can tell us.

The experiment to decide if a hardware or algorithm cause of the
super-linear speed-up is to try the single thread version by having it
run 4 searches one after another, and merging results. I.e. if that
gives a performance boost over one long search then the cause must be
algorithmic? (?)

Darren


-- 
Darren Cook, Software Researcher/Developer
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