Energy-efficient computing for extreme-scale science

David Donofrio, Leonid Oliker, John Shalf, Michael F. Wehner, Chris Rowen, Jens Krueger, Shoaib Kamil, Marghoob Mohiyuddin
Lawrence Berkeley National Laboratory
Computer, Volume 42, Issue 11, p.62-71, Nov. 2009


   title={Energy-efficient computing for extreme-scale science},

   author={Donofrio, D. and Oliker, L. and Shalf, J. and Wehner, M.F. and Rowen, C. and Krueger, J. and Kamil, S. and Mohiyuddin, M.},






   publisher={IEEE Computer Society}


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A many-core processor design for high-performance systems draws from embedded computing’s low-power architectures and design processes, providing a radical alternative to cluster solutions. The computational power required to accurately model extreme problem spaces, such as climate change, requires more than a business-as-usual approach. Building ever-larger clusters of commercial off-the-shelf (COTS) hardware will be increasingly constrained by power and cooling-with power consumption projected to be hundreds of megawatts for exascale-class problems according to recent DARPA and DOE reports. It makes more sense therefore to leverage the considerable innovation of the low-power architectures developed for embedded computing markets and design a machine capable of the exaf lops performance (1 billion-billion floating-point operations per second) required for this and similarly demanding scientific applications.
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