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."
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".
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.
...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!
The conference proceedings are not online yet. So I am not sure. I could not even find the title of the talk on the conference web page
I know people who are at SIAM PP and they are all : "why are they talking about PP on slashdot ?". There was no major anouncement. I'll check the proceedings again next week, but I believe there is no major improvement. IBM is probably just trying to get some more light.
We can find the following IBM talks in yesterday page :
http://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=9507
The paper have the same author and name than this paper published last year :
http://portal.acm.org/citation.cfm?id=1645413.1645421
So they are probable publishing an improvement on their 2009 work.
"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.