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

[Alexandre Torres Porres]

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
these.

cheers


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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