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What doesn't scale well across multiple cores? Give me a few examples. The signals fired by the human brain are pretty slow compared to what computers can do, however the brain does massive amounts of processing, because everything is wired in parallel with billions of connections.

If you can solve the parallel problem first, getting the individual processing units running at a faster rate will be the trivial task.

A first issue related to multicore is that if the input of task N in an algorithm depends on the output of task N-1, you're screwed. This prevents many nice optimizations from being applied.

A purely mathematical example : prime factorization of integers from 1 to 10000.

First algorithm that comes to mind is...

For N from 1 to 10000

..For I from 1 to N

....If I divides N then

......Store I in divisors of N

This algorithm can be scaled across multiple cores quite easily (just split the first for loop). But in order to waste a lot less processing power when N grows large, we may be tempted to use this variation of the algorithm...

For N from 1 to 10000

..For I from N to 1

....If I divides N then break

..Add I to divisors of N

..Add divisors of I to divisors of N

...which can't be scaled across multiple cores because it relies on the order in which the Ns are enumerated !

Member since:

2010-03-08

This has been discussed a billion times already Some things just don't scale well accross multiple cores, if they scale at all. As an example, for physics simulations, interactions can become a major nuisance when you have parallel processing in mind (they are a nuisance for all kinds of calculations, anyway).

So along with the current trends towards hundreds of low-performance processor cores, making individual cores faster is still a good thing for some problems, as long as the bus bottleneck and some relativistic issues concerning the size of electronic circuitry can be worked around ^^