[PD] Super computer made of legos and Raspberry Pi computers

Alexandre Torres Porres porres at gmail.com
Sun Sep 16 22:47:22 CEST 2012

Thanks a lot Andy, that was really informative.

So I see there's no point at all comparing this "super" Pi rack to general
computers, and that you can't run one Pd having it being served by 64 of


2012/9/16 Andy Farnell <padawan12 at obiwannabe.co.uk>

> On Sun, Sep 16, 2012 at 10:24:45AM -0300, Alexandre Torres Porres wrote:
> > now my question is;
> >
> > spending 4k to build a Pi supercomputer can give you more power and
> > possibilities than with a top of the line MAC for example (which will
> cost
> > just as much, and be a quad core 2.7 intel i7, 1.6GHz bus, 16GB Ram).
> We keep using the word 'supercomputer', and maybe a bit of
> perspective would help clarify matters of scale.
> Back in the mists of time /\/\/\ ...... wavy lines ...../\/\/\
> A computer that a small business might own could be moved by one person
> if they really needed the exercise. After the 1980s they were called
> microcomputers and you could pick one up and carry it.
> A minicomputer had a special room of its own, and was between ten and
> maybe fifty times faster. You could get a good one for a hundred thousand
> dollars. Minis were generally for mid level industrial organisations.
> Notice the power factor here between the everymans computer and the
> "top of the range" generally available model, which has remained constant.
> The biggest price differential is over the smallest value curve, as
> you would expect in commercial mass market.
> A mainframe was an order of magnitude more powerful than a standard
> computer, having a whole floor to itself. Mainframes are generally
> for bulk data processing and were owned by governments or very
> large corporations. They were characterised by IO, rows of tape machines
> and teleprinters, more like a giant computerised office.
> A supercomputer is, by definition, that which is on the cutting edge of
> feasible research. Most supercomputers are in a single location and not
> distributed or opportunistic, they usually have a building dedicated to
> them and a power supply suitable for a small town of a thousand homes
> (a few MW). A team of full time staff are needed to run them. They cost a
> few hundred million to build and a few tens of millions per year to
> operate.
> Current supercomputers are measured in tens of Peta FLOPS, ten to a hundred
> times more powerful than the equivalent mainframe, and are primarily
> used for scientific modelling.
> To put this operational scale versus nomenclature into todays terms
> (taking into account one order of magnitide shift in power );
> A microcomputer would probably be classed as a wearable, embedded or
> essentially invisible computer operating at a few tens or hundreds
> of MFLOPS, costing between one and ten dollars and operating
> from a lithium battery. If you have active RFID ID your credit card
> probably has more CPU power than an early business computer.
> The Raspberry Pi, gumsticks, and PIC based STAMPs occupy this spectrum.
> The word minicomputer now tends to denote a small desktop, notebook
> or smartphone, or anything that is considered 'mini' compared
> to the previous generation, and probably having the capabilities of a
> full desktop from two or three years ago.
> A powerful standard computer, the kind for a gaming fanatic or
> at the heart of a digital music/video studio is about five to ten
> times as powerful as the smallest micro (a much smaller gap than
> one might think) despite the large difference in power consumption
> and cost. Thse run at a few GFLOPS.
> What used to be a 'minicomputer' is now what might be used in a
> commercial renderfarm, essentially a room of clustered boxes
> costing tens of thousands of dollars and consuming a
> heavy domestic sized electricity bill. Total CPU power in
> the range of 10 GFLOP to 1 TFLOP
> The current guise of the 'mainframe' is what we would now see as a
> Data Center, a floor of an industrial unit, probably much like
> your ISP or hosting company with many rows of racked indepenedent
> units that can be linked into various cluster configurations
> for virtual services, network presence and data storage.
> Aggregate CPU power in the region of 10 TFLOP to 0.5 PFLOP
> Supercomputers are still supercomputers, by definition they are
> beyond wildest imagination and schoolboy fantasies unless
> you happen to be a scientist who gets to work with them.
> A bunch of lego bricks networked together does not give you 20PFLOP,
> so it does not a supercomputer make.
> However, there is a different point of view emerging since the mid
> 1990s based on concentrated versus distributed models. Since the
> clustering of cheap and power efficient microcomputers is now
> possible because of operating system and networking advances,
> we often hear of amazing feats of collective CPU power obtained
> by hooking together old Xboxes with GPUs, (Beowulf - TFLOP range)
> or using opportunistic distributed networks to get amazing power
> out of unused cycles (eg SETI at home/BOINC and other volunteer
> arrays, or 'botnets' used by crackers) (tens to hundreds of TFLOPS).
> Some guides to growth here with interesting figures on the estimated
> cost per GFLOP over the last 50 years:
> https://en.wikipedia.org/wiki/FLOPS
> > I'm guessing that CPU wize it would be more powerful indeed; even thought
> > it's a modest one, that's 64 cores against 4...
> So the issue now is that a parallel model of computing needs the
> problem cast into a program that works in this way. Some algorithms
> are trivially rewritten to work well on clusters, but many are not.
> The aggregate power isn't a full indicator of the expected speedup.
> A multi-core has fast data connection between cores but little
> memory for each processor, whereas a cluster may have GB of
> memory associated with each node but much slower data throughput
> between nodes.
> > what I'm not familiar to is how supercomputing works and optimizes the
> work
> > by splitting it into all CPU units.
> This is an important area of computer science. In summary, if the overhead
> of splitting a subproblem, sending it to node/core,
> collecting the result and re-integrating it back into the end solution
> is less than it would cost to compute it on a more powerful single node,
> then you have a speedup. This is where algorithm design gets fun :)
> Message passing protocols serve to split up the data according to
> schemes that mirror the algorithm, a bit like routers in the internet.
> Wavefront broadcast, bifurcation, all manner of schemes are used to
> break up and reassemplbe the sub-processes. Anderw Tannenbaum wrote
> one of the early and very accessible books on it all, called "Distributed
> Operating Systems"
> If _all_ the data needs to be present everywhere in the system then
> distributed models fail because the data throughput problem starts
> to dominate the advantage gained by parallel computation. So, only
> certain kinds of program can be run on 'supercomputers' that work
> this way. Your average desktop application like Protools probably
> wouldn't benefit much running on the IBM Sequoia, because it isn't
> written to get advantage from that architecture.
> cheers,
> Andy
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