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Energy-efficient cluster computing with FAWN: workloads and implications

Published: 13 April 2010 Publication History

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.

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Published In

cover image ACM Other conferences
e-Energy '10: Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
April 2010
239 pages
ISBN:9781450300421
DOI:10.1145/1791314
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IFIP TC6
  • University of Passau

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New York, NY, United States

Publication History

Published: 13 April 2010

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Author Tags

  1. cluster computing
  2. design
  3. energy efficiency
  4. flash
  5. measurement
  6. performance

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

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  • (2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
  • (2021)State-of-the-Art Power Management TechniquesProceedings of Second Doctoral Symposium on Computational Intelligence10.1007/978-981-16-3346-1_18(223-236)Online publication date: 20-Sep-2021
  • (2020)A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud EnvironmentIEEE Access10.1109/ACCESS.2020.29927488(94279-94293)Online publication date: 2020
  • (2019)HEXOProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3325408(85-96)Online publication date: 17-Jun-2019
  • (2019)Design trade-offs for emerging HPC processors based on mobile market technologyThe Journal of Supercomputing10.1007/s11227-019-02819-4Online publication date: 23-Mar-2019
  • (2018)Recent Trends in Green Cloud ComputingProceedings of 2nd International Conference on Communication, Computing and Networking10.1007/978-981-13-1217-5_93(947-956)Online publication date: 8-Sep-2018
  • (2017)A Survey and Taxonomy of Energy Efficiency Relevant Surveys in Cloud-Related EnvironmentsIEEE Access10.1109/ACCESS.2017.27180015(14066-14078)Online publication date: 2017
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  • (2016)An experimental evaluation of datacenter workloads on low-power embedded micro serversProceedings of the VLDB Endowment10.14778/2947618.29476259:9(696-707)Online publication date: 1-May-2016
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