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August 30, 2010
CERN's Large Hadron Collider has proved to be an interesting test case for ultra-large-scale data collection and computation. The project, which generates petabytes of data for its particle acceleration mission, requires one of the largest and most widespread compute grids in the world. Less than a year after the collider began fully operating, the LHC project is already blazing a path for eScience.
The dominant computational theme of the LHC work is data reduction. With petabytes of data being generated every second by the LHC detectors, the challenge is to filter out the uninteresting information so that the critical data can be more easily sent to secondary sites for storage and processing.
According to an Ars Technica article penned by John Timmer, in general, the LHC cyberinfrastructure is performing even better than expected. Although a 35-year-old datacenter is forcing higher density compute clusters (and water cooling), the robust network and improving price-performance of disks have lessened the project's reliance on tape storage. Writes Timmer:
One of the reasons for the increased reliance on disks is the network that connects the global grid to CERN. "Because the networking is going so well, filling the pipes can outrun tapes," von Rueden told Ars. Right now, that network is operating at 10 times its planned capacity, with 11 dedicated connections operating at 10Gbps, and another two held in reserve. Each connection goes to one of a series of what are called Tier 1 sites, where the data is replicated and distributed to Tier 2 sites for analysis. Von Rueden said that the fiber that powers this setup has been "faster, cheaper, and more reliable than in planning."
An interesting aspect to the LHC setup at CERN is that they've decided to limit hardware support contracts to no longer than three years. The rationale is that because price-performance for hardware is rising so rapidly, it doesn't pay to keep any particular machine running too long; when it breaks down, just replace it with something cheaper and faster.
Full story at Ars Technica
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
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).
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.