System and Architecture Level Characterization of Big Data Applications on Big and Little Core Server Architectures

Abstract

The rapid growth in data yields challenges to process data efficiently using current high-performance 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. Low-power embedded cores in servers such as little Atom have emerged as a promising solution to enhance energy-efficiency to address these challenges. Therefore, the question of whether to process the big data applications on big Xeon- or Little Atom-based servers becomes important. In this work, through methodical investigation of power and performance measurements, and comprehensive application-level, system-level, and micro-architectural level analysis, we characterize dominant big data applications on big Xeon- and little Atom-based server architectures. The characterization results across a wide range of real-world big data applications, and various software stacks demonstrate how the choice of big- versus little-core-based server for energy-efficiency is significantly influenced by the size of data, performance constraints, and presence of accelerator. In addition, we analyze processor resource utilization of this important class of applications, such as memory footprints, CPU utilization, and disk bandwidth, to understand their run-time behavior. Furthermore, we perform micro-architecture-level analysis to highlight where improvement is needed in big- and little-core microarchitectures to address their performance bottlenecks.