Olof Bjarnason wrote:
[snip]
A short question after having read through most of this thread, on the
same subject (time-optimizing CPython):
http://mail.python.org/pipermail/python-list/2007-September/098964.html
We are experiencing multi-core processor kernels more and more these
days. But they are all still connected to the main memory, right?
To me that means, even though some algorithm can be split up into
several threads that run on different cores of the processor, that any
algorithm will be memory-speed limited. And memory access is a quite
common operation for most algorithms.
Then one could ask oneself: what is the point of multiple cores, if
memory bandwidth is the bottleneck? Specifically, what makes one expect
any speed gain from parallelizing a sequential algorithm into four
threads, say, when the memory shuffling is the same speed in both
scenarios? (Assuming memory access is much slower than ADDs, JMPs and
such instructions - a quite safe assumption I presume)
[ If every core had it's own primary memory, the situation would be
different. It would be more like the situation in a distributed/internet
based system, spread over several computers. One could view each core as
a separate computer actually ]
Don't forget about the on-chip cache! :-)
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