Google, IBM to team up for "parallel" computing
www.chinaview.cn 2007-10-09 09:03:20   Print

IBM and Google on Monday announced that they will build large data centers to train students for web-based software development.

IBM and Google on Monday announced that they will build large data centers to train students for web-based software development. (File Photo)

    BEIJING, Oct. 9 (Xinhuanet) -- IBM and Google on Monday announced that they will build large data centers to train students for web-based software development.

    Google is building a data center that will contain more than 1,600 processors by the end of the year. IBM is also setting up a data center for the initiative.

    The centers would allow a larger number of students and programmers to have access and processing power for writing software code involving massive amounts of data over the Internet, a practice known as "parallel computing," or "cloud computing."

    They are also offering software and a university curriculum focused on parallel computing, which spreads out computing tasks among dozens or hundreds of computers.

    The pilot program, which is already under way at the University of Washington, will add students and professors from the Massachusetts Institute of Technology, Carnegie-Melon University, the University of Maryland, Stanford University, and the University of California at Berkeley.

    "The goal of this initiative is to improve computer science students' knowledge of highly parallel computing practices to better address the emerging paradigm of large-scale distributed computing," said IBM in a statement.

    "It's no longer enough to program one machine well," argued Google senior software engineer Christophe Bisciglia. "To tackle tomorrow's challenges, students need to be able to program thousands of machines to manage massive amounts of data in the blink of an eye. "

    Parallel computational computing is the process by which one common task is broken down into a multitude of data packets that are simultaneously processed across multiple servers.

    The method can be used to improve efficiency for common tasks and to more easily complete difficult tasks that require extreme amounts of processing power, such as gene sequencing.

    (Agencies)

Editor: Wang Yan
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