2009/10/22 MRAB <pyt...@mrabarnett.plus.com> > 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! :-) >
Sorry for continuing slightly OT: Yes, that makes matters even more interesting. Caches for single-cpu-boards speed up memory access quite dramatically. Are caches for multi-core boards shared among the cores? Or do each core have a separate cache? I can only imagine how complicated the read/write logic must be of these tiny electronic devices, in any case. Of course caches makes the memory access-operations must faster, but I'm guessing register instructions are still orders of magnitude faster than (cached) memory access. (or else registers would not really be needed - you could just view the whole primary memory as an array of registers!) So I think my first question is still interesting: What is the point of multiple cores, if memory is the bottleneck? (it helps to think of algorithms such as line-drawing or ray-tracing, which is easy to parallellize, yet I believe are still faster using a single core instead of multiple because of the read/write-to-memory-bottleneck. It does help to bring more workers to the mine if only one is allowed access at a time, or more likely, several are allowed yet it gets so crowded that queues/waiting is inevitable) -- > http://mail.python.org/mailman/listinfo/python-list > -- twitter.com/olofb olofb.wordpress.com olofb.wordpress.com/tag/english
-- http://mail.python.org/mailman/listinfo/python-list
