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This paper presents the architecture and motivation for a cluster-based, many-core computing architecture for energy-efficient, data-intensive computing. FAWN, a Fast Array of Wimpy Nodes, consists of a large number of slower but efficient nodes coupled with low-power storage. The authors present the computing trends that motivate a FAWN-like approach, for CPU, memory, and storage. They follow with a set of micro-benchmarks to explore under what workloads these FAWN nodes perform well (or perform poorly), and briefly examine scenarios in which both code and algorithms may need to be re-designed or optimized to perform well on an efficient platform.
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