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Breaking Supercomputers' Exaflops Barrier
Nerval's Lobster writes "Breaking the exaflops barrier remains a development goal for many who research high-performance computing. Some developers predicted that China's new Tianhe-2 supercomputer would be the first to break through. Indeed, Tianhe-2 did pretty well when it was finally revealed — knocking the U.S.-based Titan off the top of the Top500 list of the world's fastest supercomputers. Yet despite sustained performance of 33 petaflops to 35 petaflops and peaks ranging as high as 55 petaflops, even the world's fastest supercomputer couldn't make it past (or even close to) the big barrier. Now, the HPC market is back to chattering over who'll first build an exascale computer, and how long it might take to bring such a platform online. Bottom line: It will take a really long time, combined with major breakthroughs in chip design, power utilization and programming, according to Nvidia chief scientist Bill Dally, who gave the keynote speech at the 2013 International Supercomputing Conference last week in Leipzig, Germany. In a speech he called 'Future Challenges of Large-scale Computing' (and in a blog post covering similar ground), Dally described some of the incredible performance hurdles that need to be overcome in pursuit of the exaflops barrier."
That reminds me, what exactly IS that top secret program they use the Department of Energy's super computer for? Certainly not securing stockpiles of nuclear weapons as claimed.
http://energy.gov/articles/department-energy-supercomputer-helps-design-more-efficient-big-rigs
The other claim was some top secret military thing. Another NSA thing?
How is exaflop a barrier? Is there some atypical difficulty in exceeding an exaflop?
My take away from reading this and the blog post is that, while NVIDIA may consider graphics to be their bread & butter, it looks like they're looking at this space (HPC) very seriously in the long term--perhaps they even think they can dominate it. This is a big difference from the other players: IBM isn't bothering to throw POWER at it, and AMD/ATI is only present on older machines; ATI in particular seems more interested in going after the mobile space rather than HPC. I don't know what to make of Intel other than they know they're the choice for the non-GPU side and are at the top of their game.
One problem I see is that NVIDIA is still a fabless house and has performance limitations tied to whatever fab they partner with; perhaps this is why they downplay process gains in the blog post.
Of course, if the conspiracy theorists are to be believed, NSA and friends already have this 10-years-into-the-future technology...
Hmm, Mr. Fusion is due in a couple of years...
“He’s not deformed, he’s just drunk!”
All the talk about who has the fastest / most awesome computer in the world used to make sense --- there were a lot of problems which need huge computational power to help solve
They went from mere gigaflop to petaflop and now they are aiming all the way to break the exaflop barrier
Now, let me ask this --- is there really a case which justifice all the juice ?
From giga to peta, it's already a difference of 1,000 times
From peta to exa, another 1,000
Which means, when they finally break the exa-barrier, they already attain 1,000,000 (one million times) the crunching power of what they used to get, in the giga era
Do they really need the 1,000,000 fold of crunch to solve their problem, or has this been turning into another "pene" contest ?
Muchas Gracias, Señor Edward Snowden !
Oops, sorry,
Should have used "tera" in place for "giga" ...
Muchas Gracias, Señor Edward Snowden !
I can hear mosquito flying it is so quiet. Not so long ago, when US had the fastest supercomputer I was bombarded by government propaganda of success. TV stations, newspapers and radio were singing the same song. We are the greatest, we rule, you filthy rest of the people.
Why the silence now?
Imagine a beowulf cluster of.... What? All supercomputers are basically beowolf clusters now? Umm...Ok, is Natalie Portman still topical?
It's pretty simple: Accuracy and detail of physics simulations.
Science is a constant strive for more of both accuracy and detail. Both of these are constrained by processing power and programming ability.
With more processing power, we are able to develop more accurate simulations which will help further understanding about the physical world, which will in turn give us cause to revise the programs that run on them to get more and more complex and accurate... requiring more processing power, yet again.
The only possible end to this struggle is when we have enough knowledge and processing power to simulate with *perfect* accuracy every single sub-sub-sub-atomic particle in the universe in realtime.
Well I don't know anything at all about nuclear simulations and fluid dynamics modeling...
But for pure benefit to mankind I'd say folding@home is a pretty worthy project. It's been running for years and has helped make actual discoveries and raised understanding of protein folding's effects.
According to Wikipedia it was running at 14 Petaflops when last updated. Would taking that up to an exaflop be a huge benefit? You bet!
How about being able to simulate an entire life cycle of a human body at atomic scale? That would gain us tremendous understanding of well... EVERYTHING.
Most definitely there are worthy projects that have a real need for exaflop computing and it's not a waste of time.
You remind me of my friend who years ago said that his 802.11b wireless network was as fast as he'd ever need. Guess he didn't plan on people watching multiple HDTV streams throughout the house.
Cwm, fjord-bank glyphs vext quiz
So if we JUST put roughly 30 of the Tianhe-2s or 500,000 nodes with 100,000,000 computing cores in one big system, we'd have our exascale computer!
Anyone want to venture a guess how long it'd take Intel to make 1,000,000 Xeons and 1,500,000 Phis?
I can't wait to see the day, but me thinks we have a long way to go!
I can't believe some folks thought the Tianhe-2 was going to be the one to break the exaflop barrier! OOPS, only made it 3% of the way there...
Cheers!
Hey at least when a "n-word" troll snags first post, you may as well reply to it and thereby VALIDATE IT because that way, you get a post very close to the top! At the very top for those who don't browse at -1, which I think is most users seeing how that is default.
So you got yours, that's all that matters, right? Who cares if you had to obtain the help of a total jackass to get it?
This is where the Science part of "computer Science" comes in. Sometime you just have to see if you can, and assume the practical application will come later.
It would foolish to assume that we will *never* need an exaflop of processing power :).
Does anyone have an idea of what these extremely expensive systems are even for? And don't say password cracking/NSA, because both of those tasks are "embarrassingly parallel", so that you can use a cloud of separate computers rather than a tightly interlinked network like a supercomputer.
Are there real world problems right now where another 100x more CPU power would make real, practical differences? (versus making the algorithm more efficient, etc)
we all know Chinese numbers represent a value exactly 14% less than what the rest of the world agrees on.
Hey racemixer, anti-nigger trolling is MY gimmick. Go find your own.
Shut up, nigger!
God damn it you little mother fucker, I don't give a fuck who or where you are. If you don't stop stealing my shit, I'll hunt you down and rape your sorry ass with my HIV+ dick.
The human brain can perform 10^16 operations per second. A machine that can perform 10^18 operations per second might be able to simulate a human brain and become the first sentinet machine.
This has been predicted for decades, and it will be known as the singularity. It is a very big thing. Your life will change in unimagineable ways in the decade following the singularity. All for the better.
That would almost be enough to run Vista!
But for pure benefit to mankind I'd say folding@home is a pretty worthy project. It's been running for years and has helped make actual discoveries and raised understanding of protein folding's effects.
According to Wikipedia it was running at 14 Petaflops when last updated. Would taking that up to an exaflop be a huge benefit? You bet!
While not wishing to critisise folding@home specifically, we should be careful not to assume that there is an automatic progression from data to knowledge to understanding and hence to benefit. And with rising costs (both financial and environmental) we should not blindly assume that building huge supercomputers or running millions of inefficient home computers 24/7 is an inherently good idea.
You know, it's comment like this is why I rarely bother coming back to slashdot anymore. I used to enjoy reading the comments but now it is populated by whiny assholes who seem uninterested in exploration, science and just doing something because it's cool. Lety's just turn them all into server farms and not build big computers, eh?
What could we do with an exaflop computer? What couldn't we do!
Then there is some prick below spouting nuggets of wisdom 'knowledge != benefit' as if that actually fucking means something.
Does it make sense to rate supercomputers with the speed of solving a dense linear system?
Do we really have such huge and unstructured linear systems that need to be solved directly (LU factorization with partial pivoting)?
Can somebody list such applications?
Moore's law predicts that the "factor-of-33" will be bridged in about 10 years. There is only a factor of 20 to the "peak performance", so about a year before that, peak performance might topple the exabyte "barrier".
(Some people plug in different constants in Moore's law. I use factor-of-1000 for every 20 years. That's 30 every 10, 2 every 2, and about 5 every five. This has never failed me: it always works out).
artificial intelligence will never match natural stupidity
You know, it's comment like this is why I rarely bother coming back to slashdot anymore.
You know, it's comments like this is why i think you're a total dweeb, and everyone knows you really can't get enough of /. while you're sitting there cooped up in your "command center" in your mom's basement stuffing your pizza face with McDonald's fries.
And no doubt behind Slashdot you have a bunch of tabs with Google image searches for "boobies".
And while you're "raping" me (and I'm trying to figure out whether you're little HIV+ prick is actually in my ass yet) I'll be pounding your sister's ass and fingering your mom's raggedy old snatch.
We're at 5.4% of exaflop scale. Somehow I don't think this is a 2013 / 2014 goal ;)
Some developers predicted that China's new Tianhe-2 supercomputer would be the first to break through.
Wait... *what* uninformed developer(s) predicted that? The previous record (six months ago) was set by Titan, at 17.59 Petaflop/s. So to pass the exaflop barrier this time around would require over a fifty-fold improvement -- something never before seen in the history of the Top500 list. Did someone *really* make this prediction, or is author Kevin Fogarty just making shit up?
Tom Geller
Why do you republicans even bother with this stuff?
I keep saying Seti@Home is the most potentially beneficial. Just an old Galactic National Geographic talking about "science of the ancients" will advance the human condition by more than all this other stuff put together.
Unless the expensive Exadata box we just bought isn't capable of the exa-stuff they promised.
none
A more pessimistic estimate would say Moore's law only gets you a doubling every 3 years nowadays, so a factor of 32 would take 15 years to work out. See the troubles there were for e.g. TSMC moving to 28nm, and now 20nm.
An exaflops supercomputer would still be possible, with a 10x boost from Moore's law over 10 years and building a 3x bigger supercomputer.
Mainy important science problems are at least 4D in nature: three space dimensions and time. These include weather prediction, seismic prospecting, fluid dynamics, etc. 5.6 to the fourth power is one thousand in creased cost. You either get a finer grid or larger grid.
I heard a NOAA talk in Boulder about the erroneous Hurricane Sandy prediction. The "European" weather model correctly predicted the rare west turn of the northeastern hurricane while the US models did not. The Europeans used a 20 km would wide grid model and the SU a 32 km grid cell. The Europeans had more powerful computers. The US runs more frequent "incremental" model updates, while the Europens run fewer full calculations.
They are much lower power and somewhat lower speed than desktop CPUs. You'd have to use many more of them. Some projects are trying this.
The human brain can perform around 200-2,000 Petaflops (0.2 - 2 Exaflops) - when we compare it to computers.
The problem is that we can only access the conscious part, which is probably in the range of 100-200 Flops (Note, no mega, giga, or tera).
The subconscious part is where the real processing power lies. If we could simulate that in a computer, it would be tremendous in maybe understanding how it works.
For example: the human brain has the ability to "foresee" the future within a timeframe of around 0.2 seconds (or less). How does it do this? How does that work? Is it only a result of huge processing power or something else? Does two exaflops result in consciousness?? Questions after questions...
This comment is more insightful than funny. I doubt a machine could ever reproduce this condition. When is the last time you met a stupid person? Sure, at the time I'm it was very frustrating. But even to this day, you remember a lot of them, and it makes you feel good. Doesn't it?
i meet a lot of stupid people... the world is full of them
the post i was replying to was funny because even though a machine may be approaching the operational speed of the human brain, the software running on it will never be comparable to the synaptic pathways of the human brain (which aren't even very well understood to begin with).
you may be able to calculate pi to the millionth decimal place in a fraction of a second with one of these exaflop machines when they come out, but how long will it take for it to have an idea?
The advantage of Xeon Phi cards is that parallelization on those cards works similar like classical parallelization on supercomputers.
Not really, no. Classic supercomputers were vector machines whereas Xeon Phi is wide SIMD.
You just use MPI
MPI is equally applicable to GPU or Xeon Phi, it operates at a level above the raw computation. In both cases you have controlling CPUs with accelerators attached (GPU in once case, Xeon Phi in the other). MPI is used to manage the data flow between these units but has little to do with the architecture of those units themselves.
For GPUs, on the other hand, you have to adapt a lot of code.
You have to adapt code either way:
For GPU you express the problem as a scalar kernel that is executed in parallel. You have to make sure that the work doesn't overlap but you only have to consider one element at a time.
For SIMD you break your problem in to SIMD-width chunks that are computed in parallel. It is easier to synchronise operations but you have to fit the problem into chunks of the right size.
Xeon Phi has an advantage where you have existing SIMD code (e.g. SSE), but if you are starting from scratch then there is no clear winner. And HPC code is increasingly being written in languages like OpenCL and CUDA which are designed for GPU rather than SIMD.