Slashdot Mirror
IBM Claims Breakthrough Energy-Efficient Algorithm
jitendraharlalka sends news of a claimed algorithmic breakthrough by IBM, though from the scant technical detail provided it's hard to tell exactly how important the development might be. IBM apparently presented its results yesterday at the Society for Industrial and Applied Mathematics conference in Seattle. The breathless press release begins: "IBM Research today unveiled a breakthrough method based on a mathematical algorithm that reduces the computational complexity, costs, and energy usage for analyzing the quality of massive amounts of data by two orders of magnitude. This new method will greatly help enterprises extract and use the data more quickly and efficiently to develop more accurate and predictive models. In a record-breaking experiment, IBM researchers used the fourth most powerful supercomputer in the world... to validate nine terabytes of data... in less than 20 minutes, without compromising accuracy. Ordinarily, using the same system, this would take more than a day. Additionally, the process used just one percent of the energy that would typically be required."
I guess they stopped using Windows Vista?
Sounds like someone found a faster algorithm (maybe just constants), and since energy efficiency is the hot new thing, "faster" is now translated into "saves energy".
Can it organise my porn?
I'll buy three!
What do they do exactly?
-- There are three kinds of mathematicians: those who can add and those who can't.
Can someone please clarify exactly what they've achieved here? All I hear is that they can somehow sift through large quantities of data much quicker. What kind of data? What are they trying to extract? And for what end?
Why OpalCalc is the best Windows calc
This would be a real story if it gave implementation details, but it doesn't even tell us what the algorithm does; therefore it's totally worthless. Get this crap off the front page.
Without more information, this really sounds like they just had a horribly-slow-but-at-least-it-works algorithm in the first place and now done some work on making it more efficient. They don't even say what type of processing was being done on the data..
which is totally what she said
...for analyzing the quality of massive amounts of data...
I have an algorithm that does that in O(1):
return "Not the best quality, but pretty good.";
My UID is prime. Hah!
With faster algorithms, the machine can just get more jobs done in the same amount of time. But the jobs will just keep coming, so the energy use never changes.
Or are the new algorithms SO fast that all processing needs of humanity will be done in a week, thereby allowing us to turn off all supercomputers? Now that would save energy.
THL phish sticks
The funny thing about energy efficiency is that it saves companies money, but they get to spin it as being "green." [For example, when grocery stores eliminate plastic bags to be "green," what they really mean is they're eliminating bags to be "cheap."] If this new algorithm has no penalty associated with it, then it saves time and energy, therefore money and "the environment."
I had but a simple dream, to destroy all humans.
Now you just need the brains. Brains to design the system, brains to drive the investigation, and brains to try to improve the algorithms the system uses. ... Er, but what are we going to do with all the people who just don't "have" the brains?
Mmmm, brains ...
You don't realize that to post his comment, mcgrew traveled back in time from 2104 using his cell phone.
You are welcome on my lawn.
regularly produce this magnitude of algorithm speedup...
The MAFIAA is a bunch of mindless jerks who will be the first up against the wall when the revolution comes
As a computer engineer, I'm fascinated by the potential improvements in performance.
As a wired citizen, I'm terrified of the additional data-mining capabilities this will provide to our corporate overlords.
We are the 198 proof..
Running this once should reduce your PC's energy consumption to near zoro:
The only problem is, with all that jumping around and swordplay, Zorro ends up using tons of energy.
When you're afraid to download music illegally in your own home, then the terrorists have won!
The fans on servers have variable speed. Case closed.
I believe that you will find those fans can still run at variable speeds with the case open.
What?
When you're afraid to download music illegally in your own home, then the terrorists have won!
Here's a link with actual content on what the algorithm does:
http://www.hpcwire.com/features/IBM-Invents-Short-Cut-to-Assessing-Data-Quality-85427987.html
AH you are not seeing the real potential of this.
It took them 20 mins to do. Meaning they can do 70 more customers in one day. Charge 10% more for the same job. Poof 70x1.1xcost gross profit. Oh and it cost them 99% less power wise. Margin just went up by 99%. Meaning they can also undercut competitors in the field. Or resize the computer so it still takes a day and still sell by the same price point. So it just costs them 99% less to do powerwise and customers pay the same amount.
Wont cost them a thing. In fact I would be willing to bet they make even more. Wait until the MBA's are done spinning it.
I think he gets his insults from moveon.org.
With faster algorithms, the machine can just get more jobs done in the same amount of time. But the jobs will just keep coming, so the energy use never changes.
Or are the new algorithms SO fast that all processing needs of humanity will be done in a week, thereby allowing us to turn off all supercomputers? Now that would save energy.
Hey now! Everyone knows that 5 IBM mainframes covers the entire world market for computers.
"Low cost high performance uncertainty quantification", full text available in PDF.
http://portal.acm.org/citation.cfm?id=1645421&coll=GUIDE&dl=GUIDE&CFID=77531079&CFTOKEN=42017699&ret=1#Fulltext
And, here's the abstract:
Uncertainty quantification in risk analysis has become a key
application. In this context, computing the diagonal of in-
verse covariance matrices is of paramount importance. Stan-
dard techniques, that employ matrix factorizations, incur a
cubic cost which quickly becomes intractable with the cur-
rent explosion of data sizes. In this work we reduce this
complexity to quadratic with the synergy of two algorithms
that gracefully complement each other and lead to a radi-
cally different approach. First, we turned to stochastic esti-
mation of the diagonal. This allowed us to cast the problem
as a linear system with a relatively small number of multiple
right hand sides. Second, for this linear system we developed
a novel, mixed precision, iterative refinement scheme, which
uses iterative solvers instead of matrix factorizations. We
demonstrate that the new framework not only achieves the
much needed quadratic cost but in addition offers excellent
opportunities for scaling at massively parallel environments.
We based our implementation on BLAS 3 kernels that en-
sure very high processor performance. We achieved a peak
performance of 730 TFlops on 72 BG/P racks, with a sus-
tained performance 73% of theoretical peak. We stress that
the techniques presented in this work are quite general and
applicable to several other important applications.
ok, mr. anonymous, I work with all those wares, and the differences aren't *that* big, some percentage points in certain situations with certain hardware and certain transactions. and the very fastest way to run databases doesn't involve open source software, tpc.org will tell you all about that. it happens Oracle or DB2 on a big HP/UX or AIX is going to whoop open source ass with usual business needs on mid and large systems, but at huge cost and with vendor lock-in and limitations to customization and integration with other systems.
It will make money for IBM if potential customers look at it and realize that they too, will save money by buying IBM.
hah, only on slashdot can someone post exaggerated urban legends and be marked interesting while a dose of reality gets "troll"
Man, the energy industry doesn't like it when five more people start riding bikes. They dispatch armies of lobbyists to Washington if there's even a hint that the EPA mileage standards are going to increase by three miles per gallon.
You best believe they're going to pay attention if computers become more energy efficient. You know how much of the nation's energy bill is because of computer use?
Me neither, but I bet it runs into a whole bunch of money.
You are welcome on my lawn.
For anyone who's interested in what these guys have done- the WHPCF'09 paper by Bekas and Fedulova (and going back a bit further, their 2007 paper by Bekas et al.) give the details.
In many statistical problems we end up with the problem of finding the diagonal entries of the inverse of a known symmetric and positive definite matrix A. For example, in linear regression the variances for the fitted parameters are found on the diagonal of inv(X'*X). When this matrix A is very large, the computation can be very expensive, since it requires O(N^3) time by conventional methods (Compute the Cholesky factorization of A and then use the Cholesky factors to solve for N right hand sides.)
Bekas et al. have developed a Monte Carlo approach that can give good (e.g. 2-3 digits of accuracy) estimates of the diagonal entries in inv(A) by using an interative method to approximately solve systems of linear equations involving A. The approximate iterative solutions take roughly O(N^2) time, and there are s of these systems to solve, where sN. Thus the computational complexity is lowered from O(N^3) to roughly O(N^2). Furthermore, you can solve these s systems of equations in parallel. Going one step further, you can do a lot of the computation in single precision, so it can be done on GPGPU's and other machines that don't do double precision floating point efficiently.