Global-Local View: Scalable Consistency for Concurrent Data Types

  • Deepthi Akkoorath
  • José Brandão
  • Annette Bieniusa
  • Carlos Baquero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11014)


Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Many applications apply ad-hoc techniques to eliminate the need for synchronous atomic updates, which may result in non-linearizable implementations. We propose a new model which leverages such patterns for concurrent access to objects in a shared memory system. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. These local updates become visible to other threads only after the thread-local view is merged with the global view. This enables better performance at the expense of linearizability. We discuss the design of several datatypes and evaluate their performance and scalability compared to linearizable implementations.


Data Availability Statement and Acknowledgements

The work presented was partially supported by EU H2020 LightKone project (732505), and SMILES Research Line within project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact /NORTE-01- 0145-FEDER-000020” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

The datasets and code generated during and/or analysed during the current study [5] are available in the figshare repository:


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Deepthi Akkoorath
    • 1
  • José Brandão
    • 2
  • Annette Bieniusa
    • 1
  • Carlos Baquero
    • 2
  1. 1.Technical University of KaiserslauternKaiserslauternGermany
  2. 2.HASLab, Universidade do Minho and INESC TECBragaPortugal

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