> The bus problem is a general one. E.g. modern graphic cards have a very 
> powerfull GPU. One could use this e.g. for the computation of neural 
> networks. The theoretic speedup is impressive, but the practical is low or 
> it even slows down things. The neural-network-computation must - in 
> comparision to the data - very large. Otherwise the transfer of data eats up 
> all the speedup.

this reminds me of two 'graduate student' hacks i got to see in the early 90's.
the SGI machines at that time had special-purpose hardware
to do point rotations and translations, and a giant bank of high-speed RAM that 
would
otherwise go unused if you weren't doing graphics.  the thinking was, hey, why 
not
break down this NxN matrix multiplications into little 4x4 pieces, and use the
graphics ram as if it were freely available for any particular purpose.  feed
the data array in as if it were a group of points describing an object that you
needed to rotate, hit it against the 4x4 chip with parameters representing 
little
4x4 chunks of the larger NxN transformation, and read your data out of the 
graphics
RAM as it became available.

another cute trick that i still laugh about was a solution to the 'there are
no machines available for you to use' problem.  the affected student rewrote 
his code
in postscript and sent it to the printer.  sure, it tied up the cheap processor 
in the printer
overnight, but then it printed out the results.  :)

s.





 
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