Understanding big data analytics workloads on modern processors

Zhen Jia, Jianfeng Zhan*, Lei Wang, Chunjie Luo, Wanling Gao, Yi Jin, Rui Han, Lixin Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Big data analytics workloads are very significant ones in modern data centers, and it is more and more important to characterize their representative workloads and understand their behaviors so as to improve the performance of data center computer systems. In this paper, we embark on a comprehensive study to understand the impacts and performance implications of the big data analytics workloads on the systems equipped with modern superscalar out-of-order processors. After investigating three most important application domains in Internet services in terms of page views and daily visitors, we choose 11 representative data analytics workloads and characterize their micro-architectural behaviors by using hardware performance counters. Our study reveals that the big data analytics workloads share many inherent characteristics, which place them in a different class from the traditional workloads and the scale-out services. To further understand the characteristics of big data analytics workloads, we perform correlation analysis to identify the most key factors that affect cycles per instruction (CPI). Also, we reveal that the increasing complexity of the big data software stacks will put higher pressures on the modern processor pipelines.

Original languageEnglish
Article number7736117
Pages (from-to)1797-1810
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume28
Issue number6
DOIs
Publication statusPublished - Jun 2017
Externally publishedYes

Keywords

  • Big data analytics
  • Micro-architectural characteristics
  • Performance optimization
  • Workload characterization

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