Raspberry Pi server clusters

Hello sir,

I'm an Undergrad who is interested in cluster computing and parallel computing and i would like to do this as my final year project to analyze signals and do some VLSI based simulations. I would like to know how you are going to deal with raspberry pi's architechture because it has fairly less limitations in terms of horse power when you compare to the A9 processor in Apple TV which LMU Scientists used to construct a cluster.

Thank you,

Kishore Kumar

Hello sir,

I'm an Undergrad who is interested in cluster computing and parallel computing and i would like to do this as my final year project to analyze signals and do some VLSI based simulations. I would like to know how you are going to deal with raspberry pi's architechture because it has fairly less limitations in terms of horse power when you compare to the A9 processor in Apple TV which LMU Scientists used to construct a cluster.

Thank you,

Kishore Kumar

@freads: Clustering Pi boards for High Performance Computing (HPC) is not an effective use of the Pi.  I wrote about this at more length earlier in this thread, article #14.  What's more, Eben Upton said the same thing in the recent Element 14 webinar.

My clustering requirements are not for HPC (as I explained before), but it sounds like yours are, since signal analysis and VLSI simulation are very demanding applications.  What's more, you will be quite likely to hit your head against the Pi's limited amount of memory in your application.

On the face of it then, I would guess that you may be misapplying the Pi.

Morgaine.

in a different forum you would be told that a cluster of Pis is called

a bramble, and is a reasonable thing to do.

Oh but the comedy value of the fanbois over there is priceless!  Credit where credit is due.

My master thesis was on parallel computing.

The important thing to realize is that some applications are latency limited, and others are bandwidth limited.

In short, suppose you're doing a weather simulation, then after each timestep, for each cell you need the (new) pressure and other parameters for the neighboring cells to be able to do the next calculation for the current cell. If that information was calculated on another node, you'll have to wait for that information to go back and forth between those two nodes.

On the other hand, some applications do not need the feedback from the previous step on the other node, so all that matters is the CPU speed, and the bandwidth between nodes.

In fact the raspberry pi isn't good at any of these. Networking would go through USB which incurs a 1ms latency every time you try to do something. USB is slow, and the CPU isn't fast.

But still an interesting experiment. In an education environment, you can for example teach students to program the 64-raspberry-pi-cluster  and then later let them run for real on the bigger-better-faster cluster. For about $4000 you'd have yourself a good teaching-cluster. :-)

Hmmmmm... Adding 64 thin-bevel(*) widescreens would give you an impressive (15k x 8k) videowall. Wow!

(*) Or what is the rim around the screen called?

Roger Wolff wrote:

> The important thing to realize is that some applications are latency limited,

> and others are bandwidth limited.

The important thing to realize is that some parallel applications are neither latency limited nor bandwidth limited, because not all parallel applications are in the area of HPC.  But I already explained that point in post #14 and then again in #20.

The fact that Rpi can offer neither good bandwidth nor good latency (nor good CPU performance) is immaterial in such applications, of which there are many.  Rpi is not likely to be effective for supercomputing, as Eben Upton explained in answer to a question.  But clusters are certainly not limited to HPC.

What's more, your reference to video walls happens to provide a rather nice example of an application that can make good use of a cluster of Rpi boards without being latency limited nor bandwidth limited, for the simple reason that it typically uses no IPC at all.

Hey, everyone knows that Rpi isn't as powerful as conventional supercomputer, but there are some applications which can make use of this low computing power, things such as swarm robotics, 3d printer etc can take this job pretty well, instead of writing linear code you write parallel code, weather simulation is a big deal with Rpi, leave small miniaturized supercomputers built for stuff like biological analysis like Little Fe http://littlefe.net/ .Check the link. And also the LMU project done by scientist is just a demonstration that anything can be done with ARM.

I still need to do more research to come to a conclusion. Guess this will be my final year project with some parallel code running on it.