What is the key difference between Fork/Join and Map/Reduce?
Do they differ in the kind of decomposition and distribution (data vs. computation)?
One key difference is that F-J seems to be designed to work on a single Java VM, while M-R is explicitly designed to work on a large cluster of machines. These are very different scenarios.
F-J offers facilities to partition a task into several subtasks, in a recursive-looking fashion; more tiers, possibility of 'inter-fork' communication at this stage, much more traditional programming. Does not extend (at least in the paper) beyond a single machine. Great for taking advantage of your eight-core.
M-R only does one big split, with the mapped splits not talking between each other at all, and then reduces everything together. A single tier, no inter-split communication until reduce, and massively scalable. Great for taking advantage of your share of the cloud.