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Supercruncher Applications
starheight writes "Bill McColl has written an article contrasting traditional massively parallel supercomputing with a whole new generation of compute-intensive apps that require massively scalable architectures and can deliver both incredible throughput and real-time responsivenes when processing millions or billions of tasks."
Looking at his examples (Search, Ecommerce, Software-as-a-Service, Infrastructure-as-a-Service, Fraud Detection) I have to think "wow, single point of failure". Lots and lots of fault-tolerance needed to put all your eggs in one basket like that.
No folly is more costly than the folly of intolerant idealism. - Winston Churchill
The first half of his list seems a bit flighty. They lean more towards buzz and less useful applications. But the second half is much more practical and likely. There are many potentially interesting applications coming up, but I don't think we'll directly see most of them publicly on the internet. So I give him a +0.5 Insightful.
Developers: We can use your help.
"Using supercomputers to test the next-generation version of the SMP code, we get good scaling to many more cores than in the Intel prototype, and we expect to do even better in the future."
m l#166684
http://forum.folding-community.org/fpost166684.ht
http://fahwiki.net/index.php/SMP_client
My main side project is real time ray tracing software. It is very nearly not subject to Amdahl's Law. In the terminology of the Wiki article, F is approximately zero for Ray Tracing. It will scale very well past 10 cores and may well be able to make good use of 100 cores. Memory bandwidth seems to be the limiting factor (that determines F) but that may not be a problem with enough cache and good code. It's also the only potential mass-market use for a lot of cores. nVidia your days are numbered.
Additionally, many of these computers don't run just 1 application. IBM's blue gene, and many other Dept. of Energy/Defense/* computers run a large number of research applications, ranging from 10's to 1000's of cores. It is very rare that a single program gets to run on such a large machine for any length of time by itself, so in most cases, programs don't have to scale to 100,000 PE's, but rather they scale to hundreds or a few thousand. Far more applications can scale well to hundreds than thousands, and still have reasonable speedup.
Microsoft Sucks, F/OSS Rocks. I get mod points now right?
Bill McColl, for those who aren't familiar with him, was the driving force behind the bulk synchronous parallel (BSP) model of programming. This model, while available in the MPI-2 spec, is not widely used as is. Instead, its major contribution is inspiring remote direct memory access and the partitioned global address space, among others.
Last time we spoke, Bill said that he was interested in the issue of massively scaled computers that can handle fault tolerance pre-emptively. He compared today's supercomputers (Blue Gene, Cray XT4, Altix, etc) to a racing car that was really fast for a few hours a week, but wasn't even reliable enough to get the groceries. He was also interested in computers that can handle a continuous influx of data (as his blog post mentions), similar to managing millions of RSS feeds.
An example application domain for this stuff would be Wall Street firms that have to run time series analysis on streaming data. Prof. McColl is really on the right track here.
What? You are on drugs, yes? And not the good kind?
What about video encoding? Besides codec parallelism, you can also parallelize the distance between two keyframes, handing that chunk off to a core (or node) for processing. This is very mass-market - more and more people want to make snazzy home movies.
In fact, far more people would like to do this than render 3d movies.
"You're right," Fisheye says. "I should have set it on 'whip' or 'chop.'"
Amdahl's law isn't really a problem, its just a thing. The law of gravity is not a problem, its just a thing.
Supercomputing is really cool with embarasingly parallel problems and things like superlinear speedup. Supercomputing is a mess because its basically a hack. Funding and support are always issues. Even though we buy thousands of CPUs at a time, they are still a blip on the radar compared to regular server sales, and vendors don't cater to supercomputing because ironically, its not much of a market for the systems.
Now, to actually read the article and see what we are talking about
In most cases, researchers request a specific number of cores, based on experience of how well their code scales. Some codes to auto-scale, depending on available cores, but these are rarer. The way it works is in a batch queue system... Users submit a job required 2000 cores, and wait until that many are available. Then, when the cores become available, their job runs for 6-48hrs or more, depending on the job. In most cases, a large number of researchers are often in contention for computing time, and wait their turn in line. The good ones tend to understand the system better, and will submit workloads that reflect the current available resources, thus limiting the time their work spends sitting in the queue.
Microsoft Sucks, F/OSS Rocks. I get mod points now right?
Some off-the-wall future consumer things to consider:
I think things get particularly interesting thinking about many-core in handheld devices. Software radio could be tremendously useful there. Route planning using a network of GPS-enabled handheld devices would be cool. Background searching could be used for a lot of things.