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Clusters vs Supercomputers

Despite cluster hype, users say supercomputers may be the better fit for some applications

Resch believes that one of the best things about clustering is cost. ”The good thing about the cluster is that with a small amount of money, small research groups can get a reasonable amount of performance."

Sharan Kalwani, a high-performance computing specialist at General Motors Corp., agrees that clusters are not ideal for every type of application. “Clusters work only for a certain class of problem,” he says. “The I/O bandwidth is not there.”

Kalwani, who has used both clusters and supercomputers at GM, tells NDCF that clusters are more appropriate for highly compute-intensive applications that need little I/O. “Always use the right tool for the right job,” he notes.

For its part, GM has taken the supercomputer route for its crash testing and design, unveiling a new IBM Corp. (NYSE: IBM) system last year. This helped push the firm’s supercomputer capacity up from 4,982 gigaflops, or billions of operations per second, to over 11,000 gigaflops, according to Kalwani (see GM Buys Major IBM Supercomputer and IBM Speeds GM Crash Tests).

Clearly, time is money in the automobile industry. With the new supercomputer, GM can get its cars to market within 18 months, Kalwani told attendees at Oak Ridge. This is a stark contrast to nine years ago, when it took a full 48 months to design and launch a car, and Kalwani says GM is looking to push this envelope even further. “I have just been handed my next assignment,” he says. “It’s a year!”

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