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Gordon: using flash memory to build fast, power-efficient clusters for data-intensive applications
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Architectural Support for Programming Languages and Operating Systems archive
Proceeding of the 14th international conference on Architectural support for programming languages and operating systems table of contents
Washington, DC, USA
SESSION: Power and storage in enterprise systems table of contents
Pages 217-228  
Year of Publication: 2009
ISBN:978-1-60558-406-5
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Authors
Adrian M. Caulfield  University of California, San Diego, San Diego, CA, USA
Laura M. Grupp  University of California, San Diego, San Diego, CA, USA
Steven Swanson  University of California, San Diego, San Diego, CA, USA
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

As our society becomes more information-driven, we have begun to amass data at an astounding and accelerating rate. At the same time, power concerns have made it difficult to bring the necessary processing power to bear on querying, processing, and understanding this data. We describe Gordon, a system architecture for data-centric applications that combines low-power processors, flash memory, and data-centric programming systems to improve performance for data-centric applications while reducing power consumption. The paper presents an exhaustive analysis of the design space of Gordon systems, focusing on the trade-offs between power, energy, and performance that Gordon must make. It analyzes the impact of flash-storage and the Gordon architecture on the performance and power efficiency of data-centric applications. It also describes a novel flash translation layer tailored to data intensive workloads and large flash storage arrays. Our data show that, using technologies available in the near future, Gordon systems can out-perform disk-based clusters by 1.5× and deliver up to 2.5× more performance per Watt.


REFERENCES

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Collaborative Colleagues:
Adrian M. Caulfield: colleagues
Laura M. Grupp: colleagues
Steven Swanson: colleagues