HPCwire: Model Based Computing

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March 16, 2007

Model Based Computing

by Michael Feldman, HPCwire Editor

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For the past year or so, Intel has been talking up RMS, an application software model for terascale computing. RMS, which stands for Recognition, Mining and Synthesis, is the class of software that Intel believes will represent the killer apps of the not-too-distant future -- 2010 and beyond. These applications will require the capabilities of terascale processors -- teraflop performance processing terabytes of data.

RMS applications are used to manipulate complex models, which can be either objects or events. The three components of RMS describe its function. Recognition involves defining the model; mining has to do with sifting through datasets for instances of that model; and synthesis performs "what-if" types of calculations on the model to yield predictions or solutions.

For example in financial markets, there is a deluge of real-time data being generated for all types of traded options (bonds, stocks, currency). This makes qualitative financial analysis somewhat of a guessing game. RMS might change that. A trader would be able to define a model of an attractive options investment (recognition). Once an investment of this type is found (mining), an algorithm could be applied to provide the trader with the different levels of risk and potential return (synthesis) in a given economic environment. The application doesn't provide the trader with an answer, per se, just guidance on which investments are more likely to yield good returns under different sets of economic conditions (interest rates, currency rates, stock P/E ratios, etc.).

Other examples can be found in a variety areas, such as medicine (cancer treatment), security (terrorist threat identification), communication (real-time language translation), retail (virtual apparel shopping), and transportation (automated vehicle control), to name a few. In fact, it would be hard to find an area of human endeavor that would not be able to take advantage of RMS systems. After all, virtually everything we encounter in the real world can be modeled.

"It's a very powerful paradigm for dissecting problems," says Jerry Bautista, Director of Technology Management for Intel's Microprocessor Research Laboratory and co-director of Intel's Terascale Program. "It has tremendous application in almost anyplace you look. We're trying to use the computer to suggest a course of action. That's a lot more powerful than Google, where you're just trying to find something."

The fact that RMS application must manipulate whole models, rather than lower level constructs, not only means that more computing power is required, but also more sophisticated software.

"The problem is ordinary computers don't model things," said Pradeep Dubey, Senior Principal Engineer, Manager of Innovative Platform Architecture Microprocessor Technology Lab, Intel. "Aside from supercomputers, today's computers aren't capable of developing mathematical models of complex objects, systems or processes."

An important feature of this model is the interactive nature of the applications. This works on a couple of different levels. The first is that the types of applications being envisioned usually have to deal with real-time data input, and then turn around and generate timely output. For the options trading application above, it means that the investment predictions must be timely; an opportunity may no longer be relevant after a few hours, or perhaps even minutes.

Another aspect to the interactiveness is the ability of the software to learn, through the feedback of the mining and/or synthesis operations. Again using the example of the options trading app, the application could track actual performance of trades it identified and use that information to refine the original model of what defines an attractive investment.

"In this new approach, it doesn't matter how good the model is to begin with," explains Dubey. "The use of real-time feedback loops can make the software much more powerful than static applications."

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