August 19, 2010
The Army Research Laboratory (ARL) Defense Department Supercomputing Resource Center (DSRC) in Maryland is used to conduct research in the areas of weapons development and simulation-based testing, chemistry, nanoscience, and beyond with processing power at 350 trillion calculations per second. If the director of the ARL DSRC, Charles, Nietubicz has his wish, that capability will be delivered a web portal, and a very secure one, of course.
In an interview with SIGNAL magazine, Nietubicz noted that the center’s computers could be utilized by researchers to help the department further its research and development goals if access was granted to those outside of the center. As it stands now, however, for any group to make use of the ARL DSRC must spend a great deal of time and effort before they can get started, both because of paperwork, security and other hurdles—not to mention the significant amount of time it would take the researchers to adapt to the system itself.
It is Nietubicz’s goal to follow in the footsteps of companies like Amazon, with its HPC-geared provisioning of resources as well as the greater goals of Microsoft’s Technical Computing initiative that seeks to bring supercomputing to the masses, although he does realize that it is some time away, which is not difficult to imagine the rabid security force this would require if such a goal is ever hoped to be realized.
Nietubicz told George I. Seffers at SIGNAL that, “the average engineer doesn’t use high-performance computing. Part of the reason is that it’s too hard. I’m working to develop a tipping point for high-performance.” In his terms, this tipping point is defined by supercomputing access being part of everyday life versus an elusive privilege. While the tipping point is far from being around the bend, the on-demand model of HPC is gaining traction, if only in theory, where security and data protection is perhaps the critical factor in IT.
Full story at SIGNAL
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
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