Slashdot Mirror
NEC SX-9 to be World's Fastest Vector Computer
An anonymous reader writes "NEC has announced the NEC SX-9 claiming it to be the fastest vector computer, with single core speeds of up to 102.4 GFLOPS and up to 1.6TFLOPS on a single node incorporating multiple CPUs. The machines can be used in complex large-scale computation, such as climates, aeronautics and space, environmental simulations, fluid dynamics, through the processing of array-handling with a single vector instruction. Yes, it runs a UNIX System V-compatible OS."
Of course, but the true question is...
Does it run Linux.
Cue the redundant replies and grouchy mods.
So, aside from having all of this power in one centralized spot, how does this compare to the combined power used for distributed computing projects like ClimatePrediction.net, fold@home, and any other project on Boinc?
(This would waste some of the compute power, but if the total time saved from not changing the application exceeds the time that could be saved using more of the cycles available, you win. It is this problem of creating illusions of whatever architecture happens to be application-friendly at a given time that has made much of my work in parallel architectures - such as the one produced by Lightfleet - so interesting... and so subject to office politics.)
It's a small world and it smells funny; I'd buy another if it wasn't for the money; Take back what I paid (SoM)
"Easter Island's Weather Forecasting Service believes operation of the NEC SX-9 would realize a 53% savings under Windows Server 2008 compared to under UNIX"
"SCO files umpteen bazzillion dollar lawsuit against NEC"
What's with these new fangled measurements.
I'd like to know what it is in Libraries of Congress per Jiffy
A game has objectives and is competitive, anything else is just play
I wonder how well it will do with the really cool vector games like Asteroids or BattleZone or Tempest or...
"what's your vector, Victor?"
The Kai's Semi-Updated Website Thingy
Why, that's more powerful than a cluster of 60 PS3s! I'll take three!
I'm waiting for a "-1 somepeoplejustshouldn'tgetmodprivileges" meta-moderation.
I dunno: maybe this thing could run faster at higher temperatures in lower gravity?
(/pretending to know what I'm talking about)
I used to carry a bottle of whiskey for snake bite. And two snakes. -Nefarious Wheel
Don't be too proud of this technological marvel you have created for it is nothing compared to the power of the slashdot effect.
A feeling of having made the same mistake before: Deja Foobar
Did they buy a license from SCO?
Excuse me, but please get off my Pennisetum Clandestinum, eh!
There's an interesting paper that analyzes the data accumulated in the top500 list site, which ranks the 500 most powerful supercomputers twice a year: it shows that, over time, the share of vector machines within the list is sharply declining, both in aggregated power and in number: from around 60% in 1993 to around 10% in 2003 (see Figure 3, page 6, in said paper). Still, vector machines refuse to die and always seem to maintain a presence in the top500, as is evident from the above slashdot post. Will vector machines live forever?
The only text that can ever follow the words "up to" in computing is "0.1 *". As in "speeds of up to 0.1 * 102.4 GFLOPS". Every time a marketing droid published a press release, a kitten dies.
- Adam L. Beberg - The Cosm Project - http://www.mithral.com/
Hate to burst your bubble, but while grid computing can certainly achieve strong speeds, it is not quite AS fast as you might think.
The entire SETI@HOME project (biggest grid computing project on the net) pumps out 274 teraflops. By comparison, Blue Gene L (first in series) pumps out 360 teraflops, and newer versions will achieve petaflop range, much faster than similar anticipation for grid computing projects.
Sure, you might say, that just like supercomputers evolve, so does grid computing. The problem is that a supercomputer is built for a particular purpose, while grid computing is saturated by all the stuff you can do with it (SETI, protein folding, cancer research, or whatever). Now I'm not saying any of these projects is not totally awesome, nor trying to put down the spirit of the community, but as more and more projects compete with each other for user's CPU, the individual share per project will drop. If you combine all grid computing projects put together with all supercomputers put together, the supercomputers win by a huge margin, and even if every single PC on the world would be hooked to a grid project when not used for its primary purpose, it would still unlikely to beat the sum total for dedicated supercomputer power as far as raw computational capability goes.
And it gets even worse if you account for centralized management of all the computing tasks, coordination, error and fake result checking, and simply the lag of transmitting all the grid packets across the net.
Grid computing projects are a very interesting and useful concept, but they won't ever replace supercomputers. Nor should they. They each are good for their own purpose.
However, I could see a point in time where hybrids like the Cell (one scalar processor and eight vector processors) will become so cheap that the number of vector machines will decline even more.
The idea will never die of course, I mean, hardware is so flexible nowadays that a good student could make a vector processor at home, if he had a development board with a fast Xilinx FPGA on it. But I think the decline will continue if hybrids will be used more often.
8 of 13 people found this answer helpful. Did you?
If you build it, nerds will come. Soylentnews.org
Don't forget the Storm project!
I haven't looked closely but I would guess (based on having worked at a manufacturer of vector supercomputers many years ago) that all of the machines represented on the Top 500 list are hybrid machines. All of the vector architectures I'm familiar with had a scalar processor to handle most of the housekeeping, run the OS, compilers and things like that. Vector processors aren't very good at doing things like that.
Vector excel at running through essentially loop operations. There's two components to their speed - one is the number of functional units that they have. Conceptually vector operations are applied across an entire array at once (in math speak, arrays are known as "vectors"). Hence they are automatically parallelizable and the more functional units you have the more of the operations can actually be applied in parallel. The other component, though, is their ability to run through data quickly. Since the vector knows that it will be running through a contiguous block of memory they can really get the memory system moving. Scalar processors and their caches are not designed for running in straight lines through data. It's pretty rare to see a cache that will go into a full streaming mode so they are continually starting and stopping the memory subsystem. A vector can issue prefetches for all of its data so you can build an interleaved memory system that will really move the data (we used to have 8-way interleave on our memory subsystem. The scalar didn't do all that well with that but the vector could max out the memory bus in a sustained manner).
Come on, we need to know, what is the default editor, vi or emacs? We need to know.
MMO Quests are like orgasms:
You may solo them, I prefer them in a group.
http://www.nec.de/hpc/hardware/sx-series/index.html
There are four PDFs there; the brochure is a four-colour glossy, but there is some real information. Sadly, the interesting-looking white papers are for the SX6, two generations earlier.
SX9 summary: 65nm technology, 3.2GHz clock speed, eight vector elements handled per cycle with two multiply and two add units, which is where the 102.4Gflop/CPU figure comes from. 16 CPUs in a box about the size of a standard 42U rack.
Totally absurdly fast (ten 64-bit words per cycle per CPU) access to a large (options are 512GB or 1TB) shared main memory; absurdly fast (128GB/second) inter-node bandwidth.
May contain traces of nut.
Made from the freshest electrons.
There is a video news release and interview with the project manager here: http://movie.diginfo.tv/2007/10/26/07-0502-r.php
Parallel programming is hard. Vectorized code is kind of like parallel light in that it parallelizes very narrow operations without all that messy locking and message passing.
Oh, there was one thing that the vector excelled at that OS's do a lot of - memory copying. When we instrumented our kernel (4.3 BSD derived) we found that it spent an awful lot of time in bcopy. One of the guys spent a fair amount of time implementing a "vcopy" which would use the vector to copy large blocks of memory. On our smoking fast 237 MB/s bus with 8-way interleaved memory the scalar CPU would top out at around 25/30 MB/s due to the interaction of the cache and the memory subsystem. The vector, though, could move at bus speed. Unfortunately, I don't think it ever worked as well in practice as in theory because there was a lot of overhead in getting the vector started, checking to make sure that it wasn't busy doing other work, etc. A dedicated DMA unit would give you the same effect.
How many BogoMips does it have? =)
Whenever I hear "supercomputer" and Unix I think of using a Cray and Unicos, which was the version of Unix that ran on them. Unicos was, at least the version I used, the ultimate in bare-bones Unix. I think when people think of Unix today they think of something like Linux or the BSDs or OS X, or whatever where the environment is very rich with tools. Unix on a supercomputer is not much more than an interface between your C (or Fortran) program and the bare metal; they don't (again, in my experience) make it the kind of environment you *use*...you get your code on the machine, compile it, submit it, and log off and wait for an email.
Maybe this NEC machine is different but Unix on a supercomputer is like the cockpit of a Forumula 1 race car; just there to provide a way to steer, comforts be damned.
So here's what you're missing: Vector processors aren't about doing a lot of math. True, they do that very well, but that's not where they excell. Where vector processors really shine, is in memory bandwidth. Vector operations let you use that 4Terabyte/second of memory bandwidth, and actually use it, not spend it all flushing out cache lines. On this machine, a single load instruction can fetch 2KB of data.
Cell (and many GPUs or future whatever) have the ability to do a LOT of math, but they do it on a very tiny amount of data. These vector CPUs have dozens or hundreds of memory controllers. That's a lot of RAM chips, and a lot of copper wires between memory and the CPU. I'm sure the motherboard is dozens of layers thick for all the traces. In short, you can't get all that capability on a commodity processor, because the commodity market won't pay for all the memory bandwidth, which is expensive to engineer, and expensive per-unit.
Unless/untill there is a major change in the cost of memory, and memory bandwidth, there will still be a need for special-purpose supercomputing processors. This is not to say that Cray and NEC will continue to be the people to make such a thing. I'm sure IBM could come up with a cell-derived processor with a TON of real memory bandwidth, or maybe Nvidia. The question is: will they want to? I figure there's a lot more money to be made selling videogame consoles than there is at the high-end of the supercomputer market.