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]]>The development of the algorithm was performed at IBM Research – Zurich and was presented on Thursday at the Society for Industrial and Applied Mathematics conference in Seattle. The Zurich team has been working on the software for the last year-and-a-half and they were able to patent it at the end of 2009, prior to publishing the results. The announcement this week followed a demonstration on JuGene, the Blue Gene/P system at the Jülich Supercomputing Center in Germany.

In that experiment, 72 Blue Gene racks were used to validate nine terabytes of data in less than 20 minutes. According to IBM researchers, using conventional techniques, that analysis would have consumed more than a day, and in the process, used 100 times as much energy. A sustained performance of 730 teraflops, representing 73 percent of theoretical peak, was demonstrated on the Blue Gene/P machine, and similar or even better efficiencies would be expected on smaller clusters and workstations.

The impetus behind this work is the flood of data that is fed to computers to solve real-world problems — everything from stock portfolio management to computational fluid dynamics. The data can be generated from physical sources, like financial market feeds, weather sensors, electrical grid measurement devices, and Internet streams, as well as from synthetic sources like computer models. “Essentially we live in an ocean of bits and bytes,” says Costas Bekas of IBM Research – Zurich.

The idea, of course, is to employ computers to transform all this raw data into valuable knowledge. But before that, you have to figure out how good the data is, so that the results are trustworthy. And since the collection and generation of all this information is never error-free, one must find a way to quantify all the noise and anomalies in the data.

Statistical techniques to characterize data quality have been around for a while and come under the general term *uncertainty quantification*, or UQ, for short. There are a number of methods employed for UQ analysis, including the well-known Monte Carlo technique. But one of the most powerful ones uses something called inverse covariance matrix analysis. The problem with this method is that as data sizes grow, the computational cost becomes impractical, even for the most powerful systems. For example, Bekas says a sample of one million data samples would require an exaflop of compute power. That’s roughly 1,000 times the performance of the top petaflop supercomputing systems that exist today. To compensate, people have been manually “remodeling” the data and reducing the size of the problem, but this introduces the element of human bias into the analysis.

The overarching goal of the research was to make UQ practical, not just for elite scientists on supercomputers, but for average users on computing clusters and even personal computers. And because they wanted to cover the whole range of hardware platforms, they needed to design the algorithm so that it would be highly scalable as well as fault tolerant.

The solution the IBM’ers came up with was to replace the inverse covariance matrix method with one using stochastic estimation and iterative refinement. This enabled the researchers to cast the problem as a linear system. “The key is that the number of linear systems that we solve is small,” explains Bekas. “So if you have, say, one million data samples, then you only have to solve 100 linear systems.”

According to Bekas, this model not only enabled them to parallelize the technique, but to reduce the computational cost by a factor of 100. In addition, the algorithm employs a mixed precision scheme such that the main computation can take place in single precision (or even lower), but generate results in double precision (or even higher). While most modern CPUs can’t take advantage of this particular trick, computational accelerators, like Cell processors, GPUs, and presumably even FPGAs, can use this feature to optimal effect.

Fault tolerance is a by-product of the stochastic estimation model. “If for example something goes wrong in your machine while it is solving one of the linear systems, you can safely ignore it and you can go on to the next one,” says Bekas. “On the other hand, if you were to do full matrix inversion [and] something went wrong at the end of a very large matrix calculation, then your data is destroyed.” The technique maintains accuracies of three, four, or even five digits, which according to him, far exceeds what is required for applications.

Now that IBM’s intellectual property related to the algorithm has been patented and the technology is out of the experimental stage, the next step is to begin commercialization. There is no dearth of potential applications: weather forecasting, supply chain management, nuclear weapons simulation, astrophysics, magnetic resonance imaging, and all kinds of business intelligence — essentially any analytics or modeling application where data quality is a driving issue. Perhaps the lowest-hanging fruit is financial portfolio analysis, where exposure to risk is at the very heart of the application. IBM has a Business Analytics and Optimization group within their consulting organization ready to start client engagements.

“You’d be surprised to see how many different disciplines rely on the same basic mathematical problems,” says Bekas. “And this uncertainty quantification is one of them.”

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]]>I talked briefly to Pete Beckman, the division director at Argonne’s Leadership Computing Facility (ALCF), about their overall focus on energy conservation. According to Beckman, it’s an effort that pervades the entire organization, “Across the organization, everyone has been told ‘let’s find ways to reduce power.'” In computing, that mandate gets executed in two ways.

The HPC staff in Beckman’s division are focused on practical ways to design datacenters, and supercomputers, to conserve energy. In the Mathematics and Computer Science division, researchers look at longer term solutions to more energy efficient computation. Among the initiatives Argonne has implemented already are thin clients in offices that don’t need full workstations, and software that automatically sleeps or turns off electronic and computer equipment after hours or during periods of non-use. Farther down the road? How about capturing the heat generated by the ALCF’s supers and doing something useful with it? As Beckman puts it: “no electricity should ever be wasted.”

The ALCF also made some big decisions about energy use, including their investment in IBM’s Blue Gene/P as the centerpiece of their high performance computation. Their largest system, Intrepid, is the production workhouse with nearly 164,000 cores and over 557 TFLOPS of peak performance. This system is complemented by another BG/P used primarily for testing and code development. Intrepid is number 5 on the latest TOP500 list, but for Beckman and his team, it is just as important that the system is very energy efficient — it ranks #16 on the Green500 List released in November. The systems ahead of it on that list are other Blue Gene/P systems or systems built out of IBM’s QS22 cell processor blades, another highly energy efficient option.

All told, the ALCF uses about a megawatt of power, a fraction of the amount used by less power-efficient computers at other centers. “Because the ALCF can effectively meet the demands of this world-class computer, the laboratory ends up saving taxpayers more than a million dollars a year,” said Paul Messina, director of science at the ALCF, in a statement.

Interesting stat? Left uncooled, the Blue Genes would heat up the machine room to 100 degrees Fahrenheit within ten minutes. So with all that heat, how do they save that extra $25,000 a month when it’s cold outside? The ALCF’s chilled water system uses cooling towers. According to Beckman, once the temperature falls to 35 degrees or below outside, the temperature in the chilled water system is maintained solely by the cooling towers. Although humidity control is still an issue, that’s free cooling.

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