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Energy-efficient Cluster Computing with FAWN: Workloads and Implications

Summary: Energy-efficient Cluster Computing with FAWN:
Workloads and Implications
Vijay Vasudevan, David Andersen, Michael Kaminsky
Lawrence Tan, Jason Franklin, Iulian Moraru
Carnegie Mellon University, Intel Labs Pittsburgh
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, con-
sists 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 con-
clude with an outline of the longer-term implications of FAWN that
lead us to select a tightly integrated stacked chip-and-memory ar-
chitecture for future FAWN development.
Categories and Subject Descriptors
D.4.7 [Operating Systems]: Organization and Design--Dis-
tributed Systems; D.4.2 [Operating Systems]: Storage Manage-


Source: Andersen, Dave - School of Computer Science, Carnegie Mellon University
Carnegie Mellon University, School of Computer Science


Collections: Computer Technologies and Information Sciences