Parallel computing is limited by Amdalah's Law. Having more core does not mean you can have have more speed because it's not easy to use all those cores. Most imperial languages are not designed with running codes on multiple core and few programers are taught how to design their algorithm for using a handful of cores.
I can see this platform being a good tool for students and researchers to experiment with algorithm speedups by making their sequential code, parallel.
In my parallel programming class, our teacher had to rig together a computer lab to connect the 12 quad core computers to simulate a 64 core cluster. Then again, 64 core cluster of Parallella would cost like $7000. You can get the same 64 core setup by buying 8 x 8 core consumer desktop computer for under $3000, which will still be more cost effective and probably have ten times more computing power because of the x86 architecture.
It is a more powerful expression of the benefit of scaling with parallelism. Principally, instead of scaling speed with respect to a fixed data size, you scale the data size with respect to a fixed speed.
Having more cores means you (sometimes) can have more data. You still need those parallel programmers with their parallel algorithms though :-)
I can see this platform being a good tool for students and researchers to experiment with algorithm speedups by making their sequential code, parallel.
In my parallel programming class, our teacher had to rig together a computer lab to connect the 12 quad core computers to simulate a 64 core cluster. Then again, 64 core cluster of Parallella would cost like $7000. You can get the same 64 core setup by buying 8 x 8 core consumer desktop computer for under $3000, which will still be more cost effective and probably have ten times more computing power because of the x86 architecture.
http://en.wikipedia.org/wiki/Amdahls_law