Abstract
A sorted set (or map) is one of the most used data types in computer science. In addition to standard set operations, like Insert, Remove, and Contains, it can provide set-set operations such as Union, Intersection, and Difference. Each of these set-set operations is equivalent to some batched operation: the data structure should be able to execute Insert, Remove, and Contains on a batch of keys. It is obvious that we want these “large” operations to be parallelized. These sets are usually implemented with the trees of logarithmic height, such as 2–3 trees, treaps, AVL trees, red-black trees, etc. Until now, little attention was devoted to parallelizing data structures that work asymptotically better under several restrictions on the stored data. In this work, we parallelize Interpolation Search Tree which is expected to serve requests from a smooth distribution in doubly-logarithmic time. Our data structure of size n performs a batch of m operations in \(O(m \log \log n)\) work and poly-log span.
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Aksenov, V., Kokorin, I., Martsenyuk, A. (2023). Parallel-Batched Interpolation Search Tree. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2023. Lecture Notes in Computer Science, vol 14098. Springer, Cham. https://doi.org/10.1007/978-3-031-41673-6_9
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