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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Milos Prvulovic, milos@cc.gatech.edu, 404-385-6364
Summary Sentence: Toward Data-Scalable Systems
Full Summary: Speaker: Boris Grot, EPFLTitle: Toward Data-Scalable SystemsDate: Feb. 14, 2013 Location KACB 1116W
Boris GrotToward Data-Scalable Systems
Abstract
Big data is revolutionizing the way we live, work, and socialize. At the same time, big data is taxing our compute infrastructure in unprecedented ways. In many domains, data expansion rates are dwarfing the pace of technology improvement as measured by Moore’s law, challenging our ability to effectively store and process the data. Moreover, with the hardware industry hitting fundamental limits on its ability to lower operating voltages, energy requirements in big-data applications are skyrocketing. Sustaining the pressure of big data, and delivering on its promises, requires a fundamental restructuring of our compute infrastructure for data scalability.
In this talk, I will focus on data-intensive online applications, such as web search and social connectivity. I will explain how the mismatch between application demands and existing processor architectures leads to significant inefficiencies at the datacenter level. As a first step toward data-scalable systems, I will describe Scale-Out Processors, a processor design methodology and microarchitectural support for data-intensive online processing. By tuning the processor organization to the needs of the application domain, Scale-Out Processors improve datacenter performance by over 7x within a fixed power budget versus state-of-the-art server processors.
Bio
Boris Grot is a post-doctoral researcher in the Parallel Systems Architecture Lab at EPFL. His research seeks to address efficiency bottlenecks and capability shortcomings of processing platforms for big data. Grot received his PhD in Computer Science from The University of Texas at Austin in 2011.
