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 dif\ufb01cult 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, \ufb02ash memory, and datacentric 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 \ufb02ash-storage and the Gordon architecture on the performance and power ef\ufb01ciency of data-centric applications. It also describes a novel \ufb02ash translation layer tailored to data-intensive workloads and large \ufb02ash storage arrays. Our data show that, using technologies available in the near future, Gordon systems can out-perform disk-based clusters by 1.5\u00d7 and deliver up to 2.5\u00d7 more performance per watt.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 dif\ufb01cult to bring the necessary processing power to bear on querying, processing, and understanding this data. 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