C2-HyTM: AAn Adaptive Hybrid Transactional Memory Scheme as Efficient Synchronization Mechanism for Multicore Parallel Processing

  • Shin Young-Sung
  • , Jang Jin-Su
  • , Jang Yeon-Woo
  • , Chang Jae-Woo*
  • *Corresponding author for this work

    Research output: Contribution to conferenceConference paperpeer-review

    Abstract

    Hybrid Transactional Memory(HyTM) has recently gained much interest because it combines the advantages of Hardware Transactional Memory(HTM) and Software Transactional Memory(STM). However, the existing HyTM schemes show good performance only for a specific workload. To solve this problem, we propose an adaptive HyTM scheme as efficient synchronization mechanism for multi-core environment. Our HyTM scheme can select its optimal fallback path by using a comprehensive TM coordinator, based on the characteristics of transactions. Finally, we show from our performance analysis using STAMP benchmark that our adaptive HyTM scheme can achieve about 130~160% better performance than the state-of-the-art HyTM ones, i.e., Hybrid NOrec and RH-NOrec.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages152-153
    Number of pages2
    ISBN (Electronic)9781538683194
    DOIs
    StatePublished - 2018.10.29
    Event2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom
    Duration: 2018.09.102018.09.13

    Publication series

    NameProceedings - IEEE International Conference on Cluster Computing, ICCC
    Volume2018-September
    ISSN (Print)1552-5244

    Conference

    Conference2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
    Country/TerritoryUnited Kingdom
    CityBelfast
    Period18.09.1018.09.13

    Keywords

    • Hybrid transactional memory
    • Multi-core in-memory database systems
    • Synchronization mechanism

    Quacquarelli Symonds(QS) Subject Topics

    • Computer Science & Information Systems
    • Data Science

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