But how much useful work do they do?

Mine's bigger than yours!

No doubt the sys-admins, managers and owning companies of all these exotically named 'pooters have a lovely time measuring each other up. With kudos and bragging rights being distributed accordingly.

The thing is, I can't help wondering how many of these giga, mega, tera flops (or even just "normal", non-floating point operations) actually go towards contributing to the numerical results these puppies presumably spend their days churning out. How many of these are consumed by the O/S, just keeping track of who's running which thread this precise microsecond, or which processor should handle the next interrupt from which piece of hardware. Or even simply unwinding stacks from all the subroutine / procedure / methods (depending on exactly which version of objectect-orientated FORTRAN-4 your application was coded in) that your highly elegant but operationally inefficient massively parallel visualisation uses, just so "the boss" can see a pretty screen to impress all his/her other bosses at the next measuring-up day.

We all know that software gets slower quicker than hardware gets faster. So are all these huge number of processors, the arcane architectures needed to squeeze utility from them, the operating systems they run and the highly optimising but too-hard-for-humans-to-understand compilers really producing the goods. After all, just how fast do you need to execute a gettimeofday() call?

Storage

What about storage capacity? Pure CPU speed is relevant for some problems, but petabytes of storage is starting to matter too...

@Toastan Buttar

Well I know the FZJ, LLNL and Argonne machines are used for (not primarily) lattice qcd calculations. It's pretty annoying being here in the UK since the STFC funding debacle has left us with an ageing (and crap) computer which we can't even use because we don't have enough money to pay scottish power!!

Increased computational capacity obviously increases the range and accuracy of what we can do - and academic institutions had computers long before lattice qcd had been thought of (and a fair bit before qcd had been thought of!)

Forecast

Where's the new Met Office computer in all this? It uses enough power (1.2MW, allegedly) and taxpayers money...

@northern monkey and Boris

"Increased computational capacity obviously increases the range and accuracy of what we can do.."

"...model the atmosphere in greater and greater detail in order to produce a more accurate... "

Strictly speaking, you could do all this on my laptop, but it would need more memory and it would take thousands of years to do it. It would also be ridiculously expensive to support such an effort.

The point about supercomputers is that they give you these high resolution and large dataset results in a usefully short time. Because of this, many computing activities that have obvious 'use and value' can now be performed.

Speed is a biproduct

The goal of these supercomputers is generally to solve problems that can be done with a massively parallel approach. A more accurate way to view the supercomputer's perforance is by seeing a peta-flop capable rig and assuming it is really 1024 tera-flop rigs operating in harmony.

The harmony is the challenge. Tim Spence, you're almost right. If the two supercomputers can synchronise and talk to each other sufficiently quickly, then you can consider them the same one.

The scenarios? As Boris points out, subdividing the atmosphere is one option. Another is convex hull algorithms. Nuclear reactions is another. It all works by sub-dividing the problem into as small a part as possible. You're trying to emulate the "real world" computer - which it can feasibly be thought of. The challenge is applying the outputs of each component part and feeding it into the others without lag. Doing a calculation on one sub-part of the problem iteratively is nearly pointless - you need to calculate all parts simultaneously.

@But how much useful work do they do?

"The thing is, I can't help wondering how many of these giga, mega, tera flops (or even just "normal", non-floating point operations) actually go towards contributing to the numerical results these puppies presumably spend their days churning out. How many of these are consumed by the O/S, just keeping track of who's running which thread this precise microsecond, or which processor should handle the next interrupt from which piece of hardware."

A cluster (using commodity hardware and software at least) running an application designed for parallelism delegates high-level tasks from a master node to slave nodes as appropriate, which, because they run their own local OS, manage low level stuff like interrupts themselves. For example, a master node might send high level messages instructing 100 nodes to execute a process using 100 different data sets, and leaves them to it until they return a response. Of course, it depends on the application design ;)

Is 500 enough?

Why the top 500? If it isn't fast enough to be in the top 10, nobody cares. In fact anywhere beyond the top 3 are probably not all that interesting any more.

F@H

What about Folding@Home, Granted not one single computer but 5+ Petaflop

Computer vs. Computers

These are awesome machines, but the distinction between a "super computer" and a "server farm" is somewhat blurry these days. These new machines are mostly big arrays of PCs running on a high speed interconnect. It is also the case that there are only very particular numerical problems that are easily parallelized in a way that machines like this can crunch on, without slamming into Amdahl' Law like a bug against a windshield.

In many ways, it is more impressive that programs like Oracle's DB or MS SQL Server or the Red Dog kernel can achieve parallel acceleration of 128 cores, than it is that you can parallelize giant matrix calculations. It's a pity that so much of that computer science is still trade secret.

@ Don Mitchel

Server farm != super computer.

Server farms distribute the load after which each server is reasonably independent.

A super computer still tries to act and work as a single machine, crunching on a single problem.

I expect many super computers are involved in crunching climate models. If you're modelling the atmosphere as (10km)^3 parcels of air in increments of 1 day, then changing to (1km)^3 at 1 hour requires 24000 x more calculations to crunch the same basic model and allows more precise calculation. Some of these models crunch for months at a time.

GIGO of course, unless you precisely understand what all the variables are, you'll just get bullshit results to 20 decimal places.

Re: F@H

Chris Simpson wrote:

"What about Folding@Home, Granted not one single computer but 5+ Petaflop"

What Folding@Home does is millions of slighty different small problems.

What a supercomputer does is one very large problem.

The difference is tolerance to latency.

The answer to one F@H problem is independent of the other problems and so it can be task farmed. If you're dealing with one big simulation then you need as fast as possible communication between all the CPUs otherwise you'll be waiting an eternity for your answer.

Let's say you want to do some molecular dynamics on a piece of material the size of a grain of salt. Just holding the coordinates and velocities of all the atoms would require about 10 petabytes of memory. The drive for larger simulations, smaller approximations and finer resolutions will continue to feed these machines although Amdahl's law and other software engineering problems are raising their heads.