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Adaptive hybrid transaction processing with hardware transaction memory

  • Tao He
  • , Jianxin Li*
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Many performance-sensitive software systems gain benefit from the availability of advanced hardware features, and many research works have been transited to engineering reality. For instance, restricted hardware transaction memory (HTM), a hardware feature providing hardware atomicity and isolation guarantees for transactional execution, has gained much attention in the community of database system and becomes a new kind of approach for concurrency control of transaction processing. Previous works show that hardware transactional memory is a very promising mechanism when processing typical OLTP workloads and scales well on multi-core machines. However, the high transaction abort rate caused by capacity overflow is also the major trouble when applying hardware transactional memory in transaction execution under high-contention workloads. Many methods, such as falling back to lock-based approaches, have been proposed to improve the performance when applying hardware transactional memory to transaction execution. In this paper, we analyze the penalty of abort and fall back when hardware transactional memory fails to process transactions. Based on these analyses, we exploit the strengths of hardware transactional memory with an adaptive hybrid mechanism to improve both the throughput and latency of transaction processing on the multicore systems. Our experiment on TPC-C shows that our adaptive design can remarkably improve the throughput and latency of in-memory transaction processing.

Original languageEnglish
Title of host publicationProceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
EditorsZheng Xiao, Laurence T. Yang, Pavan Balaji, Tao Li, Keqin Li, Albert Zomaya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1785-1790
Number of pages6
ISBN (Electronic)9781728120584
DOIs
StatePublished - Aug 2019
Event21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 - Zhangjiajie, China
Duration: 10 Aug 201912 Aug 2019

Publication series

NameProceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

Conference

Conference21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
Country/TerritoryChina
CityZhangjiajie
Period10/08/1912/08/19

Keywords

  • Concurrency control
  • Database
  • HTM
  • In-memory transaction
  • Keyvalue store

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