Non-blocking interpolation search trees with doubly-logarithmic running time

T.A. Brown, A. Prokopec, D.-A. Alistarh, in:, Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, ACM, 2020, pp. 276–291.

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Conference Paper | Published | English
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Abstract
Balanced search trees typically use key comparisons to guide their operations, and achieve logarithmic running time. By relying on numerical properties of the keys, interpolation search achieves lower search complexity and better performance. Although interpolation-based data structures were investigated in the past, their non-blocking concurrent variants have received very little attention so far. In this paper, we propose the first non-blocking implementation of the classic interpolation search tree (IST) data structure. For arbitrary key distributions, the data structure ensures worst-case O(log n + p) amortized time for search, insertion and deletion traversals. When the input key distributions are smooth, lookups run in expected O(log log n + p) time, and insertion and deletion run in expected amortized O(log log n + p) time, where p is a bound on the number of threads. To improve the scalability of concurrent insertion and deletion, we propose a novel parallel rebuilding technique, which should be of independent interest. We evaluate whether the theoretical improvements translate to practice by implementing the concurrent interpolation search tree, and benchmarking it on uniform and nonuniform key distributions, for dataset sizes in the millions to billions of keys. Relative to the state-of-the-art concurrent data structures, the concurrent interpolation search tree achieves performance improvements of up to 15% under high update rates, and of up to 50% under moderate update rates. Further, ISTs exhibit up to 2X less cache-misses, and consume 1.2 -- 2.6X less memory compared to the next best alternative on typical dataset sizes. We find that the results are surprisingly robust to distributional skew, which suggests that our data structure can be a promising alternative to classic concurrent search structures.
Publishing Year
Date Published
2020-02-19
Proceedings Title
Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
Page
276-291
Conference
PPOPP: Principles and Practice of Parallel Programming
Conference Location
San Diego, CA, United States
Conference Date
2020-02-22 – 2020-02-26
IST-REx-ID

Cite this

Brown TA, Prokopec A, Alistarh D-A. Non-blocking interpolation search trees with doubly-logarithmic running time. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP. ACM; 2020:276-291. doi:10.1145/3332466.3374542
Brown, T. A., Prokopec, A., & Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 276–291). San Diego, CA, United States: ACM. https://doi.org/10.1145/3332466.3374542
Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 276–91. ACM, 2020. https://doi.org/10.1145/3332466.3374542.
T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 276–291.
Brown TA, Prokopec A, Alistarh D-A. 2020. Non-blocking interpolation search trees with doubly-logarithmic running time. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP. PPOPP: Principles and Practice of Parallel Programming 276–291.
Brown, Trevor A., et al. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, ACM, 2020, pp. 276–91, doi:10.1145/3332466.3374542.

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