On the Supercomputer Technology Crisis

scoobrs writes "Experts claim America has been eating our 'supercomputer feed corn' by developing clusters rather than new supercomputer processors and interconnects. Forbes says America is playing catch-up and that the new federal budget items are too little too late. Cray is laying people off due to decreased federal spending and claims lower margin products have forced them to create products based on commodity parts. Red Storm, one of their new Linux-based products, is being delayed to next year."

Comments

I Need A RAIS

[grunt107]

Random Array of Inexpensive Servers.

If the 'supercomputers' of today are increasing performance, does it really matter the design?

Maybe that is a signal that monolithic computer tasks are best handled in a hive mentality - have the Queen issue the big orders, have the warriors performing security, have the workers transporting the goodies (data), and have the requisite extra daughters and suitors to grow the hive and assure its viability (redundancy).

The fact that it is cost-effective is even better.

There is no crisis

[0x0d0a]

Cray has been engaging in scare tactics about "America being dominated by overseas competitors" for a while, because they're terrified of losing the lucrative business contracts from government and big business, they'll pull out all the stops. They've come up in the IT press recently a couple of times.

Screw 'em. If there's a need, the market will provide. If it turns out that the important tasks can be parallelized and run on much less expensive clusters, then all that means is that we have a more efficient solution to the problem.

The classic supercomputer is the modern desktop

[iabervon]

If you really want a vector-processor supercomputer you can program in Fortran, get yourself a G5 and gcc. The PPC64 supports SIMD vector processing. For that matter, any problem which benefits from vector processing is trivial to parallelize with threads.

Trickle Down

[Anonymous Coward]

Technology first developed on the high end slowly works it's way down into the low end. What happens when the high end is no longer there.

Not that many people really need a race care, but advances in fuels, materials, engineering in race cars eventually leads to bette passenger car. And for raw performsnce, strapping together a bunch of Festivas will not get you the same as an Indy racer.

Forbes promoting socialism?

[peter303]

Forbes has been complaining that federal support of advanced computing is too little? If the government over-stimulates an industry that has too small of a market, it wil just delay the failure.
Of course the governent should continue in its current policy of funding a few leading-edge machines that are too costly to sell into the general market, but will test new technology. The governemnt itself is a customer will energy testing, weather modeling, medicine development, etc.

Clusters and supercomputers...

[gillbates]

I've seen a lot of naive comments suggesting that supercomputers are being replaced by clusters. The truth is, anyone who can replace their supercomputer with a cluster didn't need a supercomputer in the first place:

1. (compared to a supercomputer):
2. The prime advantage of an x86-based server is that it is cheap, and it has a fast processor. It is only fast for applications in which the whole dataset resides in memory - and even then, it is still the slowest of the group.
3. Clusters are a little better, but suffer from severe scalability problems when driving IO-bound processes. As with the x86 server, if you can't put the full dataset into memory, you might as well forget using a cluster. The node to node throughput is several orders of magnitude slower than the processor bus in multiple CPU systems. (6.4GB/s vs 17MB/s for regular ethernet, or 170MB/s for Gigabit)
4. Multiple CPU servers do better, but still lack the massive storage capacity of the mainframe. They work better than clusters for parallel algorithms requiring frequent syncronization, but still suffer from a lack of overall data storage capacity and throughput.
5. Mainframes, OTOH, possess relatively modest processors, but the combined effect of having several of them, and the massive IO capability makes them very good for data processing. However, their processors aren't fast at anything, and often run at 1/2 or 1/3 the speed of their desktop counterparts.
6. Supercomputers combine the IO throughput of a mainframe with the fast processors typically associated with RISC architectures (if you can still consider anything RISC or CISC nowadays). They have faster processors, more memory, and much greater IO throughput than any other category.

It used to be that the prime reason for faster computers came from the scientific and business communities. But now that the internet has turned computers into glorified televisions, the challenges have gone from that of crunching numbers to serving content:

1. Clusters are great for serving read-only content, because there's very little active synchronization required between nodes, and the aggregate IO capacity scales well.
2. Mainframes reign when it comes to IO throughput - companies that formerly had use for a supercomputer are finding that their role is shifting to more of an information-provider role; faster processors are no longer as important as fast IO subsystems.
3. Scientists aren't being trained to use the computer as a tool; most think of a computer more or less as a means of verifying their hypothesis, rather than a means of discovering possible explanations. Their primary work is done with a calculator and pencil, and only later, when they need something to back up their ideas, do they turn to a computer simulation. The computer is a verification tool, not a means of discovery.

As our economy has shifted away from a technological base to an entertainment one, the need for supercomputers has begun to evaporate. We outsource innovation overseas so that we can lounge around on the couch watching tv and drinking beer (or surfing the net and drinking beer). The primary purpose of technological innovation has shifted from that of discovering the universe to merely bringing us better entertainment.

Re:Law of Diminishing Returns

[susano_otter]

I suppose the counter-argument would go something like this:

It's true that supercomputers aren't really all that useful or necessary these days. However, it may be that a future computing problem shall arise, which requires a next-generation supercomputer to solve. So we'd be well-served to have a next-generation supercomputer fresh from R&D, to apply to the problem.

We may only encounter one or two more supercomputer-class problems, but they might be important ones. We should be prepared.

On the other hand, we may encounter a problem that can only be solved by horses. But we don't see a lot of buggy-whip subsidies these days...

Re:Complex issues that have to be solved

[jsac]

Here's the problem. On codes which need lots of data interchange, communication speed becomes the bottleneck. I don't know of anyone running a serious fluid dynamics or weather code, which are this kind of data-interchange-limited application, who gets anything near peak performance on "real-world" problems using ASCI machines. Sure, ASCI White (a 10000-node cluster) was billed as a 10-Teraflops supercomputer. Who cares, when you get 10% of peak performance if you're lucky? NOAA wanted to buy a supercomputer in the mid-90s, for weather and climate simulations. They did the requirements analysis and decided that a Japanese vector supercomputer was what they needed -- nobody in the U.S. made them anymore. Seymour Cray flipped out -- a government organization buying foreign supercomputers? heresy! -- pulled a bunch of strings, and very soon thereafter Japanese supercomputers faced a stiff tariff because the Japanese were "dumping" their product on the U.S. market. Of course, that meant NOAA couldn't get their NEC. They ended up buying some American-made cluster and getting their piss-poor 5% of peak performance. Well, two years ago, Japan brought Earth Simulator online. It's cluster of 5000 vector processors; it boasted 30 Teraflops peak performance, which was 3 times as fast as the then-current number one machine, ASCI White. And a group from NOAA went over to Japan on invitation to check the machine out. They spent on the order of a week adapting some of their current codes to the ES architecture and fired them up. And got 66% of peak performance right off the bat. How'd that happen? Well, ES cost on the order of $100 million. (By the way, as a rule, if your 'supercomputer' cost less than $10 million, it's not really a supercomputer.) Of that, about $50 million went into developing the processor interconnect -- it's a 5000-way(!) crossbar, for you EE types. With an interconnect that big and fast, the communication bottleneck which dooms the big physics codes suddenly disappears. So, yeah, the U.S. supercomputer market at its own seed corn. To see Earth Simulator jump to the top of the Top 500 was something of a slap in the face; to see it get 20 Teraflops on real-world problems was a terrible blow to the prestige of the U.S. supercomputing community. And not one we're going to easily recover from.