Ranger’s deployment marks the beginning of the Petascale Era in high-performance computing (HPC) where systems will approach a thousand trillion floating point operations per second and manage a thousand trillion bytes of data.
"Ranger is the first of the new 'Path to Petascale' systems that NSF provides to open science. It is out in front on the pathway to sustained petascale performance," said Daniel Atkins, director of the NSF's Office of Cyberinfrastructure. "This system and others to come underscore NSF's commitment to world-class, high-performance computing ensuring that the
Ranger is a collaboration among TACC, The University of Texas at
TACC Director Jay Boisseau said, "Ranger provides incredible new capabilities for computational researchers across the nation and world. Its computational power, memory and storage capacity greatly exceed anything the open science community has had access to. It takes tremendous expertise to deploy and support research on such a system as well as to use it effectively, but it is an awesome honor and responsibility for us at TACC. Together with our partners, we are excited about fulfilling the promise of Ranger by helping researchers achieve breakthrough science across domains and disciplines, discoveries that will really change the world as well as our understanding of it."
At more than one-half a petaflop of peak performance (504 teraflops), Ranger is up to 50,000 times more powerful than today’s PCs, and five times more capable than any open-science computer available to the national science community. Ranger is built on the Sun™ Constellation System which combines ultra-dense, high-performance compute, networking, storage and software into an integrated general purpose system. Ranger comprises 3,936 compute nodes in a Sun Blade™ 6048 Modular System with 15,744 Quad-Core AMD Opteron™ processors, and Sun Fire™ x4500 servers providing 1.7 petabytes of storage. More information on the Sun Constellation System is available at: http://www.sun.com/servers/hpc/sunconstellationsystem/index.jsp.
Ranger offers more than six times the performance of the previous largest system for open science research. The boost in performance offered by Ranger relative to the previously largest open science machine is comparable to reducing the flight time from New York to London to just one hour.
Ranger and other petascale systems to follow will address many of society's most pervasive grand challenges including global climate change, water resource management, new energy sources, natural disasters, new materials and manufacturing processes, tissue and organ engineering, patient-specific medical therapies, and drug design. These issues cannot be addressed or overcome without modeling and simulation.
"Our world is facing great challenges and grappling with big questions across a broad spectrum," said William Powers Jr., president of The University of Texas at
The award for Ranger represents the largest NSF grant given to The University of Texas at
Omar Ghattas, professor of geological sciences and mechanical engineering and director of the Center for Computational Geosciences at ICES, said computational scientists have been making the case for petascale systems for more than a decade.
"NSF has responded with an aggressive campaign to provide
Ghattas leads a team that will produce the highest resolution models of convection in the Earth’s mantle to date, enabling a better understanding of the evolution of tectonic deformation. Their work is emblematic of how larger HPC systems allow for more accurate simulations, finer-grained models, shorter time to results, better statistical analysis, higher-resolution visualization ? in other words, bigger, better science.
The additional power, memory and storage of Ranger will enable new research in at least three ways: First, it will allow finer grids for resolution-starved problems, leading to more accurate solution of models. Second, it will permit additional physics to be incorporated into these models, leading to higher-fidelity simulations. Third, it will allow scientists to conduct parameter sweeps for very large-scale models, which are essential for data assimilation and uncertainty quantification, leading to better tools to support decision making.
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