Highly-scalable concurrent objects

Akkoorath, Deepthi Devaki; Bieniusa, Annette

doi:10.1145/2911151.2911158

Proceedings of the 2nd Workshop on the Principles and Practice of Consistency for Distributed Data 2016

DOI: 10.1145/2911151.2911158

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Highly-scalable concurrent objects

Deepthi Devaki Akkoorath

Annette Bieniusa

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2018

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Global-Local View: Scalable Consistency for Concurrent Data Types

Akkoorath

Brandão

Bieniusa

et al. 2018

Euro-Par 2018: Parallel Processing

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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 of synchronous atomic updates, which may result in non-linearizable implementations. We propose a new programming 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 show that it is possible to maintain thread-local views and to perform merge efficiently for several data types and evaluate their performance and scalability compared to linearizable implementations. Further, we discuss the consistency semantics of the data types and the associated programming model.

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Global-Local View: Scalable Consistency for Concurrent Data Types

Akkoorath

Brandão

Bieniusa

et al. 2018

Euro-Par 2018: Parallel Processing

Self Cite

View full text Add to dashboard Cite

show abstract

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Highly-scalable concurrent objects

Cited by 1 publication

References 7 publications

Global-Local View: Scalable Consistency for Concurrent Data Types

Global-Local View: Scalable Consistency for Concurrent Data Types

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