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Hardware/Software Co-design of Global Cloud System Resolving

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Author(s): Michael Wehner | Leonid Oliker | John Shalf | David Donofrio | Leroy A. Drummond | Ross Heikes | Shoaib Kamil | Celal Konor | Norman Miller | Huiro Miura | Marghoob Mohiyuddin | David Randall | Woo-Sun Yang

Journal: Journal of Advances in Modeling Earth Systems
ISSN 1942-2466

Volume: 3;
Start page: M10003;
Date: 2011;
Original page

Keywords: Global Cloud System Resolving Models | Exascale computing | Hardware/Software co-design

ABSTRACT
We present an analysis of the performance aspects of an atmospheric general circulation model at the ultra-high resolution required to resolve individual cloud systems and describe alternative technological paths to realize the integration of such a model in the relatively near future. Due to a superlinear scaling of the computational burden dictated by the Courant stability criterion, the solution of the equations of motion dominate the calculation at these ultra-high resolutions. From this extrapolation, it is estimated that a credible kilometer scale atmospheric model would require a sustained computational rate of at least 28 Petaflop/s to provide scientifically useful climate simulations. Our design study portends an alternate strategy for practical power-efficient implementations of next-generation ultra-scale systems. We demonstrate that hardware/software co-design of low-power embedded processor technology could be exploited to design a custom machine tailored to ultra-high resolution climate model specifications at relatively affordable cost and power considerations. A strawman machine design is presented consisting of in excess of 20 million processing elements that effectively exploits forthcoming many-core chips. The system pushes the limits of domain decomposition to increase explicit parallelism, and suggests that functional partitioning of sub-components of the climate code (much like the coarse-grained partitioning of computation between the atmospheric, ocean, land, and ice components of current coupled models) may be necessary for future performance scaling.
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