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Languages and Compilers for Parallel Computing

LCPC 2015: Languages and Compilers for Parallel Computing pp 37–53Cite as

Efficient Support for Range Queries and Range Updates Using Contention Adapting Search Trees

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9519))

Abstract

We extend contention adapting trees (CA trees), a family of concurrent data structures for ordered sets, to support linearizable range queries, range updates, and operations that atomically operate on multiple keys such as bulk insertions and deletions. CA trees differ from related concurrent data structures by adapting themselves according to the contention level and the access patterns to scale well in a multitude of scenarios. Variants of CA trees with different performance characteristics can be derived by changing their sequential component. We experimentally compare CA trees to state-of-the-art concurrent data structures and show that CA trees beat the best data structures we compare against with up to 57 % in scenarios that contain basic set operations and range queries, and outperform them by more than 1200 % in scenarios that also contain range updates.

Research supported in part by the European Union grant IST-2011-287510 “RELEASE: A High-Level Paradigm for Reliable Large-scale Server Software” and the Linnaeus centre of excellence UPMARC (Uppsala Programming for Multicore Architectures Research Center).

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Notes

  1. 1.

    We perform the change to the contention statistics counter non-atomically. Thus, it is possible for a concurrent read operation to overwrite the change. Note that this does not effect the correctness of the data structure as it only affects the frequency of its adaptations.

  2. 2.

    The efficient skip list split operation splits the data structure so that on average half the keys will be in each resulting split.

  3. 3.

    We do not compare experimentally against the Leaplist [1] whose main implementation is in C. Prototype implementations of the Liplist in Java were sent to us by its authors, but they ended up in deadlocks when running our benchmarks which prevented us from obtaining reliable measurements. Instead, we refer to Sect. 2 for an analytic comparison to the Leaplist.

  4. 4.

    We use the write-preference algorithm [5] for coordination between readers and writers and the StampedLock from the Java library for mutual exclusion.

  5. 5.

    We also ran experiments on a machine with four Intel(R) Xeon(R) E5-4650 CPUs (2.70GHz each with eight cores and hyperthreading) both on a NUMA setting and on a single chip, showing similar performance patterns as on the AMD machine. Results are available online [6].

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Author information

Authors and Affiliations

  1. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Konstantinos Sagonas & Kjell Winblad

Authors
  1. Konstantinos Sagonas
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  2. Kjell Winblad
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Corresponding author

Correspondence to Kjell Winblad .

Editor information

Editors and Affiliations

  1. North Carolina State University, Raleigh, North Carolina, USA

    Xipeng Shen

  2. North Carolina State University, Raleigh, North Carolina, USA

    Frank Mueller

  3. North Carolina State University, Raleigh, North Carolina, USA

    James Tuck

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Sagonas, K., Winblad, K. (2016). Efficient Support for Range Queries and Range Updates Using Contention Adapting Search Trees. In: Shen, X., Mueller, F., Tuck, J. (eds) Languages and Compilers for Parallel Computing. LCPC 2015. Lecture Notes in Computer Science(), vol 9519. Springer, Cham. https://doi.org/10.1007/978-3-319-29778-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-29778-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29777-4

  • Online ISBN: 978-3-319-29778-1

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