Understanding Processors Design Decisions for Data Analytics in Homogeneous Data Centers

This is the authors' abstract. We don't add key points for this paper.

Our global economy increasingly depends on our ability to gather, analyze, link, and compare very large data sets. Keeping up with such big data poses challenges in terms of both computational performance and energy efficiency, and motivates different approaches to explore data center systems and architectures. To better understand the processor design decisions in context of data analytics in data centers, we conduct comprehensive evaluations using representative data analaytics workloads on representative conventional multi-core and many-core processors. After a comprehensive analysis of performance, power, energy efficiency and performance-cost efficiency, we have the following observations: contrasted with the conventional wisdom that uses wimpy many-core processors to improve energy-efficiency, the brawny multi-core processors with SMT (simultaneous multithreading) and dynamic overclocking technologies outperform the counterparts in terms of not only execution time, but also energy-efficiency for most of data analytics workloads in our experiments.