with NVIDIA and our ongoing partnership with Dell have made possible the acquisition of a leading-edge machine, one of the fastest in New York for solving a large set of scientific, engineering and multimedia problems," Miller said. "In fact, due to the advances in heterogeneous computing, this machine cost approximately 1/1000th of the two machines in New York that have peak computing power exceeding this machine. In particular, Graphics Processing Unit (GPU)-based systems are affordable by many research groups and departments offering processing capabilities thousands of times faster than traditional systems, whereas traditional high-end clusters require extraordinary funds, space, cooling, and staffing. This is also the fastest machine on the MCIL Grid for solving many computationally-intensive problems."
Professor Jack Dongarra, one of the foremost authorities on high-end computing and director of the Innovative Computing Laboratory at the University of Tennessee said, "GPUs have evolved to the point where real-world applications are easily implemented on them and run faster than on multi-core systems. Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs."
"The Tesla GPU-based clusters used by the Cyberinfrastructure Laboratory are enabling its researchers to get supercomputing performance from smaller clusters at 1/100th the cost, significantly improving the scope and size of the research problems that they can solve. More importantly they can do this while consuming less electricity, reducing the carbon footprint required to power these systems and cool the data centers that house them," Andy Keane, general manager, GPU Computing at NVIDIA, said.
The NVIDIA-based GPU-system was acquired with funds from the National Science Foundation's (NSF's) Collaborative Research Infrastructure (CRI) Program based on a joint proposal from UB (Miller), Niagara (McCourt), SUNY-GenesPage: 1 2 3 4 Related biology news :1
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