Toward Data-Scalable Systems

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 up to 7x within a fixed power budget versus state-of-the-art server processors.

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.