System and architecture level characterization of big data applications on big and little core server architectures

Abstract

Emerging Big Data applications require a significant amount of server computational power. Big data analytics applications rely heavily on specific deep machine learning and data mining algorithms, and exhibit high computational intensity, memory intensity, I/O intensity and control intensity. Big data applications require computing resources that can efficiently scale to manage massive amounts of diverse data. However, the rapid growth in the data yields challenges to process data efficiently using current server architectures such as big Xeon cores. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers. Therefore recent work advocates the use of low-power embedded cores in servers such as little Atom to address these challenges. In this work, through methodical investigation of power and performance measurements, and comprehensive system level and micro-architectural analysis, we characterize emerging big data applications on big Xeon and little Atom-based server architecture. The characterization results across a wide range of real-world big data applications and various software stacks demonstrate how the choice of big vs little core-based server for energy-efficiency is significantly influenced by the size of data, performance constraints, and presence of accelerator. Furthermore, the microarchitecture-level analysis highlights where improvement is needed in big and little cores microarchitecture.