![]() As the winner of the Big Science Challenge, Ruotti didn't pay anything, but if he had been paying his way this dream run would have cost close to $20,000. It accessed 78 terabytes of genomic data and took a week to complete. Ruotti's run used a virtual cluster of 5000 cores on average, 8000 at peak. But it would take 115 years to complete such a project on a single computer core. The results could help clinical researchers uncover treatments for certain diseases. Victor Ruotti, who in 2011 was a computational biologist at the Morgridge Institute for Research at the University of Wisconsin-Madison, wanted to scrutinize gene expression profiles of tissue samples to find the genes involved in the differentiation of human embryonic stem cells. Jason Stowe, the company's CEO, says the goal of the competition is to allow scientists to think big in framing research questions, unconstrained by the availability of computational resources. To demonstrate the research possibilities of commercial clusters to scientists working in academia, in late 2011 Cycle Computing, announced the BigScience Challenge, a competition that sought "the runts, the misfits, the crazy ideas that are normally too big or too expensive to ask, but might, just might, help humanity," according to the company's Web site. Like the other computing resources available to scientists, scientific cloud computing has a niche. The primary appeal of this approach is its relatively low cost, made possible because the clusters are made up of commodity hardware and software-readily available computing components-says Joseph Hellerstein, manager of computational discovery for science at Google.īut there are other, practical advantages. ![]() This is possible because companies have made commercial cloud platforms-Amazon Web Services, Windows Azure, Google Compute Engine, and such-available to scientists. At far-flung data centers, "elastic" clusters of computational capacity can be assembled on-demand. ![]() Today there's yet another new player on the scalable-computing scene: the cloud. And at most top universities and research institutes, scientists can access high-performance computing clusters on campus, usually for a fee. The Department of Energy and NASA also operate high-performance computing facilities, which are available to researchers whose projects are funded by those agencies. "By funding a dozen or so sites across the country, NSF ensured that every researcher gets the same access to the resources no matter where they are located." "I have been shouting FREE COMPUTING TIME from the rooftops for about 5 years now," he writes by e-mail. In fact, in addition to his other duties, Gardner is UWs campus ambassador for the National Science Foundation's Extreme Science and Engineering Discovery Environment ( XSEDE) program, which for 25 years has made computation and storage platforms available, free-of-charge to academic researchers in the United States with high-performance computing (HPC) needs. It's not that computational resources are hard to come by they are available from a variety of sources. "Even for leading labs, it is difficult to secure a thousand cores or more to use for several months for an individual project at their own institution." -Kai Kohlhoff So, he knows about resources for scientific computation. Before joining UW, he was senior scientific specialist at the Pittsburgh Supercomputing Center. He works part-time at Google, as a visiting scientist. He has run code that utilized all 100,000-plus computer processing unit (CPU) cores and 10,000-plus hard drives of the supercomputer Kraken at the National Supercomputing Center at the University of Tennessee Knoxville. In addition to being a facilitator of computational work, Gardner is a computational astrophysicist. As director of research–physical sciences at the eScience institute at the University of Washington (UW), it's Jeffrey Gardner's job to help researchers with that migration. If data driven discovery becomes the norm, more scientists will need to upgrade from their desktop computers to more powerful, scalable computing systems. CREDIT: NICSKraken/Distributed under a CC-BY 2.0 license.
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