I recently used the Qt Concurrent framework to speed up a statistical calculation. Lots of floating point but not much memory access. Basically you break up the problem, put each part's data into an object that also has a method to process the data and turn a list of these objects over to the framework. It was rather fun to see my quad core hit 100% CPU and solve the proble in 1/4 the time. It is loosely based on the Google Map Reduce process. Breaking up the data into objects also makes it easy to keep the multi-thread access running without locks.
As I see it, the main problems yet to be solved are (1) the ability to run fast on all cores when doing memory intensive operations such as image processing and (2) the ability to run multiple short operations where the thread scheduling time becomes a problem. The first is basically due to hardware limitations and won't be solved in software. The second may be solved by better thread scheduling or programming language extensions.
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