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Supercomputers To Move To Specialization?
lucasw writes "The Japan Earth Simulator outperformed a computer at Los Alamos (previously the world's fastest) by a factor of three while using fewer, more specialized processors and advanced interconnect technology. This spawned multiple government reports that many suspected would ask for more funding in the U.S. for custom supercomputer architectures and less emphasis on clustering commodity hardware. One report released yesterday suggests a balanced approach."
Specialized hardware (almost) always outperforms commodity stuff.
I use custom designed amplifiers because they work better for my application. I could buy off-the-shelf stuff (~$500~$10,000 range), but that won't be exactly what I want. I use custom software too... know why? Because it's designed specifically for the job. That same software shouldn't really be used for other fields of research, neither should my amplifiers. The thing about this stuff is that it takes a lot of time to maintain (plus initial development). That means grad students, postdocs, and technicians who may spend over 90% of their time just keeping systems in working order and/or adding features. The benefits of customized hardware/software, in this instance, is worth the headaches associated with it.
All of my optics is commodity stuff (some is rare/exotic, but it's still basically black-box purchasing). I don't have the facilities to make coated optics, nor do I need anything that specialized, so... I just buy it.
When I was in telecom, we used Oracle and Solaris and Apache. It worked, and the cost of developing the same functionality in-house was ridiculously high (plus we'd never get to designing our products that sit on top of it).
Eventually, it always comes down to a comparison between the cost (man hours, equipment, etc) of custom building and of integrating stuff from OEMs.
So, the question our labs need to answer is, does clustered COTS hardware get the job done? Supplementary to that, is it cost-effective to buy/design it in light of the previous answer?
In any field where you are pushing the limits of technology, you have to make such trade-offs. Personally, I don't care who has the absolute fastest supercomputer (measured in flops, factoring-time, whatever)... what really counts is, who does the best research with the supercomputers.
Down with Saudi Arabia!!!
The problem is that it may not be possible to match the computation of a cluster with specialized interconnects using just commodity hardware no matter how many machines you throw at it. If a simulation has a low computation to communication ratio it's scalability is bound by the perfomance of the interconnects. In this case throwing more commodity machines at the problem will actually increase the total time required to run the experiment.
There's a far more important thing to worry about - could this be the end of "Imagine a Beowulf Cluster ..." jokes? After all, the phrase "Imagine a custom-built supercomputer utilising similar technology (albeit more specialised) to that found in one of those!" doesn't exactly roll off the tongue, does it?
Do HP's Saturn or other such special-purpose processors have hard-coded higher-level functions?
Indeed, functions Cost_an_arm_and_a_leg() and Fork_over_much_dough() are hard-coded, and always return a value of "1".
The unofficial
I get tired of seeing figures that compare peak flop rates and then don't mention that actually code usage isn't keeping up at all. The Japanese (and Europeans who are allowed to buy NEC machines) are absolutely spanking the US when it comes to fluid codes (for climate modeling for example) and it is largely because they are using vector machines with their old highly optimized Fortran (or High Performance Fortran) codes. The MPP revolution in the U.S. has been manna for the CompSci community, but has set the computational physics community back by 10 years (except for those lucky bastards with embarrassingly parallel jobs).
I would give up an unnecessary body part for an Earth Simulator.
Sigs are bad for you.