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Old Apr 14, 2011, 11:49 AM   #1
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AMD Big Data in High-Performance Compute Powered by AMD Opteron

Steve Conway, IDC Research Vice President, High Performance Computing, has posted on AMD@Work blog about how Oak Ridge National Laboratory will use their Jaguar supercomputer powered by 224,000 Opteron processor cores to near real-time analyze five gigantic government databases, in an attempt to reduced fraud and save $50bn per year:

Where HPC is concerned, IDC defines data-intensive (“big data”) problems broadly to include tasks involving sufficient data volumes and complexity to require HPC-based modeling/simulation. The problems can employ structured data, unstructured data, or both. They can come from traditional HPC domains in government, industry and academia–or they can be upward extensions of commercial problems that have grown large and complex enough at the high end to require HPC. In addition, “big data” can accumulate from the multiple results of iterative problem-solving methods in sectors such as manufacturing (parametric modeling) and financial services (stochastic modeling). So, small and medium-size enterprises (SMEs) are also encountering “big data” challenges.

Some problems involve “finding a needle in a haystack,” that is, locating a discrete item that already exists in a database. This style of problem-solving usually employs relational databases (RDBMS) and traditional search methods.

Other problems are more complex and involve “finding patterns in shifting sand.” Problems of this kind tend to involve unstructured (NoSQL) data and newer methodologies and special software frameworks such as MapReduce and Hadoop. They involve similar tasks: pattern matching, scenario development, behavioral prediction, anomaly identification, and analysis of relationships using graphs. They’re for things like catching terrorists before they leave the airport, or catching bank fraud before the criminal gets the money, or protecting the US power grid before it crashes. Some of the powerful algorithms in this domain originated in classified government.

The FBI estimates that 10% of transactions in federal health care programs – Medicare, Medicaid, Veterans Affairs and so forth – are fraudulent, costing about $150 billion a year. Price Waterhouse Coopers thinks it’s three times that amount. Today, the health care data is spread across five gigantic databases. As a result, no one can see all the data at once, fraud is detected after the fact, and the government recovers only about $1 billion a year.

Oak Ridge National Lab has submitted a proposal to unify all these databases and perform fraud detection using a Cray supercomputer nicknamed “Jaguar” that features 224,000 AMD Opteron™ processor cores. This solution could save $50 billion a year by analyzing the data in near-real time. The same methods could be applied to other criminal behavior, terrorist activities and many of the other applications I mentioned.

Read the full article on AMD@Work blog.
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