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research-article

Understanding Big Data Analytics Workloads on Modern Processors

Published: 01 June 2017 Publication History

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.

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  • (2024)Orchestration Extensions for Interference- and Heterogeneity-Aware Placement for Data-AnalyticsInternational Journal of Parallel Programming10.1007/s10766-024-00771-252:4(298-323)Online publication date: 1-Aug-2024
  • (2022)High-Concurrency and High-Performance Application of Microservice Order System Based on Big DataSecurity and Communication Networks10.1155/2022/34242832022Online publication date: 1-Jan-2022
  • (2021)LogECMemProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3480852(1-15)Online publication date: 14-Nov-2021
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  1. Understanding Big Data Analytics Workloads on Modern Processors

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    Published In

    cover image IEEE Transactions on Parallel and Distributed Systems
    IEEE Transactions on Parallel and Distributed Systems  Volume 28, Issue 6
    June 2017
    276 pages

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    IEEE Press

    Publication History

    Published: 01 June 2017

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    View all
    • (2024)Orchestration Extensions for Interference- and Heterogeneity-Aware Placement for Data-AnalyticsInternational Journal of Parallel Programming10.1007/s10766-024-00771-252:4(298-323)Online publication date: 1-Aug-2024
    • (2022)High-Concurrency and High-Performance Application of Microservice Order System Based on Big DataSecurity and Communication Networks10.1155/2022/34242832022Online publication date: 1-Jan-2022
    • (2021)LogECMemProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3480852(1-15)Online publication date: 14-Nov-2021
    • (2021)A Throughput-Oriented NVMe Storage Virtualization With Workload-Aware ManagementIEEE Transactions on Computers10.1109/TC.2020.303781770:12(2112-2124)Online publication date: 1-Dec-2021
    • (2020)Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A SurveyIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.287816839:1(101-116)Online publication date: 1-Jan-2020
    • (2020)Interference Analysis of Co-Located Container Workloads: A Perspective from Hardware Performance CountersJournal of Computer Science and Technology10.1007/s11390-020-9707-y35:2(412-417)Online publication date: 1-Mar-2020
    • (2018)System and Architecture Level Characterization of Big Data Applications on Big and Little Core Server ArchitecturesACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/32290493:3(1-32)Online publication date: 23-Jul-2018
    • (2018)Scalability Evaluation of Big Data Processing Services in CloudsBenchmarking, Measuring, and Optimizing10.1007/978-3-030-32813-9_8(78-90)Online publication date: 10-Dec-2018
    • (2017)Realistic and Scalable Benchmarking Cloud File Systems: Practices and Lessons from AliCloudIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.271532728:11(3272-3285)Online publication date: 1-Nov-2017

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