September 29, 2011
Researchers at Marshall University in the United States are set to receive a new GPU-powered cluster that will allow them to make further advances in bioinformatics, climate research, physics computational chemistry and engineering.
Nicknamed “BigGreen” the cluster will boast “276 central processing unit cores, 552 gigabytes of memory and more than 10 terabytes of storage.” This, coupled with the eight NVIDIA Tesla GPUs with 448 cores each will push BigGreen into the six teraflop range—and will allow the university’s researchers to explore new areas aided by simulation and parallel computation capabilities.
This new cluster comes about following a round of NSF funding under the “Cyberinfrastructure for Transformational Scientific Discovery in West Virginia and Arkansas (CI-TRAIN) program. This is a project that seeks to advance the IT capabilities of the two states’ institutions to build more robust nanoscience and geosciences research programs in particular.
As Dr. Jan I. Fox, Marshall’s senior vice president for information technology said in a statement this week, “For example, a 3-D scan of Michelangelo’s statue ‘David’ contains billions of raw data points. Rendering all that data into a 3-D model would be nearly impossible on a desktop computer,” she said. “Using our high-performance computing capabilities, a student or professor could run that same data and produce the model in just a fraction of the time. It will literally change the way we work and do research at Marshall University.”
Fox went on to note that “the new cluster is critical to assisting researchers with their diverse objectives. He noted that this addition “makes possible scholarly innovation and discoveries that were, until recently, possible only at the most prestigious research institutions,” she said. “Along with our connection to Internet2, our students and faculty now have access to computing power, data and information we could only imagine just a few years ago."
Full story at Marshall University
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).
Read more...
The Xeon Phi coprocessor might be the new kid on the high performance block, but out of all first-rate kickers of the Intel tires, the Texas Advanced Computing Center (TACC) got the first real jab with its new top ten Stampede system.We talk with the center's Karl Schultz about the challenges of programming for Phi--but more specifically, the optimization...
Read more...
Although Horst Simon was named Deputy Director of Lawrence Berkeley National Laboratory, he maintains his strong ties to the scientific computing community as an editor of the TOP500 list and as an invited speaker at conferences.
Read more...
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.