Concurrent Balanced Augmented Trees
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3774934.3786437.pdf
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933.66 KB
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Adobe PDF
Checksum (MD5)
a7dd9fef9b8346c2f342a87588286c82
Author(s)
Wrench, Evan
Singh, Ajay
Roh, Younghun
Fatourou, Panagiota
Jayanti, Siddhartha
Ruppert, Eric
Wei, Yuanhao
Date Issued
January 28, 2026
Publisher
ACM|Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
Citation
Evan Wrench, Ajay Singh, Younghun Roh, Panagiota Fatourou, Siddhartha Jayanti, Eric Ruppert, and Yuanhao Wei. 2026. Concurrent Balanced Augmented Trees. In Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP '26). Association for Computing Machinery, New York, NY, USA, 136–149.
Version
Final published version
Abstract
Augmentation makes search trees tremendously more versatile, allowing them to support efficient aggregation queries, order-statistic queries, and range queries in addition to insertion, deletion, and lookup. In this paper, we present the first lock-free augmented balanced search tree supporting generic augmentation functions. Our algorithmic ideas build upon a recent augmented unbalanced search tree presented by Fatourou and Ruppert [DISC, 2024]. We implement both data structures, solving some memory reclamation challenges in the process, and provide an experimental performance analysis of them. We also present optimized versions of our balanced tree that use delegation to achieve better scalability and performance (by more than 2x in most workloads). Our experiments show that our augmented balanced tree completes updates 2.2 to 30 times faster than the unbalanced augmented tree, and outperforms unaugmented trees by up to several orders of magnitude on 120 threads.
Description
PPoPP ’26, Sydney, NSW, Australia
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3774934.3786437
