Energy-efficient Cluster Computing with FAWN: Workloads and Implications

Proceedings of 1st Int'l Conf. on Energy-Efficient Computing & Networking (e-Energy 2010), Univ. of Passau, Germany. April 13-15, 2010.

Vijay Vasudevan, David Andersen, Michael Kaminsky*, Lawrence Tan, Jason Franklin, Iulian Moraru

School of Computer Science
Dept. Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213

*Intel Labs Pittsburgh

This paper presents the architecture and motivation for a clusterbased, many-core computing architecture for energy-efficient, dataintensive computing. FAWN, a Fast Array of Wimpy Nodes, consists of a large number of slower but efficient nodes coupled with low-power storage. We present the computing trends that motivate a FAWN-like approach, for CPU, memory, and storage. We follow with a set of microbenchmarks to explore under what workloads these “wimpy nodes” perform well (or perform poorly). We conclude with an outline of the longer-term implications of FAWN that lead us to select a tightly integrated stacked chip-and-memory architecture for future FAWN development.

KEYWORDS: Design, Energy Efficiency, Performance, Measurement, Cluster Computing, Flash





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