Energy-efficient cluster computing with FAWN: workloads and implications

Authors:
Vijay Vasudevan;David Andersen;Michael Kaminsky;Lawrence Tan;Jason Franklin;Iulian Moraru
Affiliations:
Carnegie Mellon University;Carnegie Mellon University;Intel Labs Pittsburgh;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University
Venue:
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Year:
2010

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Abstract

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. 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.