Raspberry Pi cluster, neuron networks and brain simulation

Question

Since the RBPI (Raspberry Pi) has very low power consumption and very low production price, it means one could build a very big cluster with those. I'm not sure, but a cluster of 100000 RBPI would take little power and little room.

Now I think it might not be as powerful as existing supercomputers in terms of FLOPS or others sorts of computing measurements, but could it allow better neuronal network simulation ?

I'm not sure if saying "1 CPU = 1 neuron" is a reasonable statement, but it seems valid enough.

So does it mean such a cluster would more efficient for neuronal network simulation, since it's far more parallel than other classical clusters ?

Answer 1

Using Raspberry Pi itself doesn't solve the whole problem of building a massively parallel supercomputer: how to connect all your compute cores together efficiently is a really big problem, which is why supercomputers are specially designed, not just made of commodity parts. That said, research units are really beginning to look at ARM cores as a power-efficient way to bring compute power to bear on exactly this problem: for example, this project that aims to simulate the human brain with a million ARM cores.

http://www.zdnet.co.uk/news/emerging-tech/2011/07/08/million-core-arm-machine-aims-to-simulate-brain-40093356/ "Million-core ARM machine aims to simulate brain"

http://www.eetimes.com/electronics-news/4217840/Million-ARM-cores-brain-simulator "A million ARM cores to host brain simulator"

It's very specialist, bespoke hardware, but conceptually, it's not far from the network of Raspberry Pis you suggest. Don't forget that ARM cores have all the features that JohnB mentioned the Xeon has (Advanced SIMD instead of SSE, can do 64-bit calculations, overlap instructions, etc.), but sit at a very different MIPS-per-Watt sweet-spot: and you have different options for what features are included (if you don't want floating-point, just buy a chip without floating-point), so I can see why it's an appealing option, especially when you consider that power use is the biggest ongoing cost for a supercomputer.

Answer 2

Seems unlikely to be a good/cheap system to me. Consider a modern xeon cpu. It has 8 cores running at 5 times the clock speed, so just on that basis can do 40 times as much work. Plus it has SSE which seems suited for this application and will let it calculate 4 things in parallel. So we're up to maybe 160 times as much work. Then it has multithreading, can do 64 bit calculations, overlap instructions etc. I would guess it would be at least 200 times faster for this kind of work.

Then finally, the results of at least 200 local "neurons" would be in local memory but on the raspberry pi network you'd have to communicate between 200 of them... Which would be very much slower.

I think the raspberry pi is great and certainly plan to get at least one :P But you're not going to build a cheap and fast network of them that will compete with a network of "real" computers :P

Anyway, the fastest hardware for this kind of thing is likely to be a graphics card GPU as it's designed to run many copies of a small program in parallel. Or just program an fpga with a few hundred copies of a "hardware" neuron.