2009/10/23 Stefan Behnel <stefan...@behnel.de> > > 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) > > Modern (multi-core) processors have several levels of caches that interact > with the other cores in different ways. > > You should read up on NUMA. > > http://en.wikipedia.org/wiki/Non-Uniform_Memory_Access >
Thanks that was a good read. Basically NUMA addresses the problems I mention by creating several primary memories (called banks) - making the motherboard contain several computers (=cpu+primary memory) instead of one primary memory and several cores, if I read it correctly. > Stefan > -- > http://mail.python.org/mailman/listinfo/python-list > -- twitter.com/olofb olofb.wordpress.com olofb.wordpress.com/tag/english
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