Is It -- Finally -- Time for the Grid? (continued)
Economy and Scale
Grid computing works especially well for repetitive jobs -- calculations that need not be made in parallel, but instead can be made sequentially. Purdue University (West Lafayette, Ind.) CIO Gerard McCartney, who oversees a grid of 6,001 Linux, Windows, Solaris and Macintosh machines that talk to each other using the University of Wisconsin's Condor grid middleware, says one Purdue faculty member grabs images of viruses from an electron microscope, and then processes the images using the grid. "He could do this on a mainframe that costs millions of dollars. Our way, he essentially does it for free."
While cost savings are an important argument in favor of grid computing, the time element may be even more important. McCartney, who contends that most computers, even when in use, utilize only 20 percent or so of their capacity, cites the case of a materials scientist at Rice University who uses the Purdue grid to analyze zeolite structures. In one day, says McCartney, he may use a CPU year of computation running "fairly small calculations that have to happen thousands of times." Midway through 2007, he'd already used three million hours of computing time, at very little cost. "These are all waste cycles. That's the point," says McCartney.
There are few infrastructure requirements for grid computing. Networking capability, of course, is essential, as is the middleware that enables the machines to distribute tasks intelligently. Foster's Argonne Laboratory, with support from IBM, developed the open-source Globus software, which, he says, "addresses security, data movement, job submission, data replication" and other challenges for large grids. Other popular middleware offerings include the Sun Grid Engine (SGE) and Condor. This software is platform-independent and has the advantage, says McCartney, of being a "lightweight installation -- you're not hiring a cadre of systems programmers to make this happen." (article continues)
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