If you want to be terse, you can just use typedefs but most of ISO C++03 libraries just assume your editor can autocomplete quickly. The culture is different and absolute clarity is preferred over terseness.
There's a lot of infrastructure behind MapReduce and I bet you any implementation is going to have a lot more code in it than what is in Data.List.
Once things are in RAM (say, less than 1 GB), quicksort is the fastest algorithm (Edit: or some other optimized in-place variation on the theme such as introsort). You can parallelize across cores all you want but all that is going to do is crap up your caches and cause bus contention, making your code more complex and slower. The bottleneck is your databus not the CPU, after all what is the cost of a compare in clock cycles? This is also why mergesort is slower and parallelizing is not going to fix it.
If you want to see things that are parallelizable check out vectorizing libraries such as Thrust that run on massively parallel GPGPUs that have the bus design and RAM to handle that kind of parallelization. They are written in C++ and both the implementation and the code to use them looks almost the same like the code I posted. The compiler technology of other languages is years behind and currently can't even target these kinds of architectures.
I've seen GPU massive parallelization in Haskell and all it did was to generate C code and then invoke the CUDA compiler. This was in a research paper. In the meantime you can fire up nvcc with Thrust and sort bazillion of keys with less than 10 lines of code in C++ using a well-tested library.
Edit: BTW, anyone (and I really don't know who would be doing this) who is even thinking of running a MapReduce Haskell cluster must have lots of free time and $$$ to burn on wasted CPU cycles.
Well, I didn't mean an actual MapReduce cluster, that's just silly. I was just pointing out that Haskell compilers can convert trivially convert these sorts of things to multicore code. Parallel Haskell basically does this, converting normal code to easy multicore code.
My point wasn't about using Haskell for high performance computing, my point was that Haskell's compiler can convert normal Haskell code to multicore code without (much, if any) work from the programmer. There is a large gap in between sequential single threaded program and a full MapReduce cluster and I think there is a significant amount of software written in this gap where Haskell-like languages could help with the parallelism/concurrency. The only reason I referenced MapReduce is since its a well known example even for people who are not well versed in functional programming (its a very common concept in functional programming).
There's a lot of infrastructure behind MapReduce and I bet you any implementation is going to have a lot more code in it than what is in Data.List.
Once things are in RAM (say, less than 1 GB), quicksort is the fastest algorithm (Edit: or some other optimized in-place variation on the theme such as introsort). You can parallelize across cores all you want but all that is going to do is crap up your caches and cause bus contention, making your code more complex and slower. The bottleneck is your databus not the CPU, after all what is the cost of a compare in clock cycles? This is also why mergesort is slower and parallelizing is not going to fix it.
If you want to see things that are parallelizable check out vectorizing libraries such as Thrust that run on massively parallel GPGPUs that have the bus design and RAM to handle that kind of parallelization. They are written in C++ and both the implementation and the code to use them looks almost the same like the code I posted. The compiler technology of other languages is years behind and currently can't even target these kinds of architectures.
I've seen GPU massive parallelization in Haskell and all it did was to generate C code and then invoke the CUDA compiler. This was in a research paper. In the meantime you can fire up nvcc with Thrust and sort bazillion of keys with less than 10 lines of code in C++ using a well-tested library.
Edit: BTW, anyone (and I really don't know who would be doing this) who is even thinking of running a MapReduce Haskell cluster must have lots of free time and $$$ to burn on wasted CPU cycles.