A Concurrency-Optimal Binary Search Tree

Aksenov, Vitaly; Gramoli, Vincent; Kuznetsov, Petr; Malova, Anna; Ravi, Srivatsan

doi:10.1007/978-3-319-64203-1_42

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…A weight-balanced tree, or BB[𝛼] trees [46] is a BST where for every 𝑇 = node(𝑇 𝐿 , 𝑣,𝑇 𝑅 ), 𝛼 ≤ 𝑤 (𝑇 𝐿 ) 𝑤 (𝑇 ) ≤ 1 − 𝛼. We omit 3 Their shared-memory implementation is between 3.2-4.9x slower than CPAM for a map, reduce, and group-by style example taken from their user guide. For primitives such as map and reduce, their implementation performs up to 2 orders of magnitude worse than CPAM (see Section 10.2)…”

Section: Preliminariesmentioning

confidence: 99%

“…This means its shared-memory implementation is not ideal 3. There is extensive research on concurrent tree data structures[3,4,18,20,28,39,44]. This work is mostly orthogonal to our work.…”

mentioning

confidence: 98%

“…Secondly although it has a shared-memory parallel implementation, it is primarily designed for a distributed setting. This means its shared-memory implementation is not ideal 3. There is extensive research on concurrent tree data structures[3,4,18,20,28,39,44].…”

mentioning

confidence: 99%

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PaC-trees: Supporting Parallel and Compressed Purely-Functional Collections

Dhulipala¹,

Blelloch²,

Gu³

et al. 2022

Preprint

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Many modern programming languages are shifting toward a functional style for collection interfaces such as sets, maps, and sequences. Functional interfaces offer many advantages, including being safe for parallelism and providing simple and lightweight snapshots. However, existing high-performance functional interfaces such as PAM, which are based on balanced purely-functional trees, incur large space overheads for large-scale data analysis due to storing every element in a separate node in a tree.This paper presents PaC-trees, a purely-functional data structure supporting functional interfaces for sets, maps, and sequences that provides a significant reduction in space over existing approaches. A PaC-tree is a balanced binary search tree which blocks the leaves and compresses the blocks using arrays. We provide novel techniques for compressing and uncompressing the blocks which yield practical parallel functional algorithms for a broad set of operations on PaCtrees such as union, intersection, filter, reduction, and range queries which are both theoretically and practically efficient.Using PaC-trees we designed CPAM, a C++ library that implements the full functionality of PAM, while offering significant extra functionality for compression. CPAM consistently matches or outperforms PAM on a set of microbenchmarks on sets, maps, and sequences while using about a quarter of the space. On applications including inverted indices, 2D range queries, and 1D interval queries, CPAM is competitive with or faster than PAM, while using 2.1-7.8x less space. For static and streaming graph processing, CPAM offers 1.6x faster batch updates while using 1.3-2.6x less space than the state-of-the-art graph processing system Aspen.

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Section: Preliminariesmentioning

confidence: 99%

“…This means its shared-memory implementation is not ideal 3. There is extensive research on concurrent tree data structures[3,4,18,20,28,39,44]. This work is mostly orthogonal to our work.…”

mentioning

confidence: 98%

See 1 more Smart Citation

PaC-trees: Supporting Parallel and Compressed Purely-Functional Collections

Dhulipala¹,

Blelloch²,

Gu³

et al. 2022

Preprint

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Optimal Concurrency for List-Based Sets

Aksenov

Gramoli

Kuznetsov

et al. 2021

Lecture Notes in Computer Science

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Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM

Koval

Федоров

Sokolova

et al. 2023

Lecture Notes in Computer Science

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This paper presents , a new practical and user-friendly framework for testing concurrent algorithms on the Java Virtual Machine (JVM). provides a simple and declarative way to write concurrent tests: instead of describing how to perform the test, users specify what to test by declaring all the operations to examine; the framework automatically handles the rest. As a result, tests written with are concise and easy to understand. The framework automatically generates a set of concurrent scenarios, examines them using stress-testing or bounded model checking, and verifies that the results of each invocation are correct. Notably, if an error is detected via model checking, provides an easy-to-follow trace to reproduce it, significantly simplifying the bug investigation.To the best of our knowledge, is the first production-ready tool on the JVM that offers such a simple way of writing concurrent tests, without requiring special skills or expertise. We successfully integrated in the development process of several large projects, such as Kotlin Coroutines, and identified new bugs in popular concurrency libraries, such as a race in Java’s standard and a liveliness bug in Java’s framework, which is used in most of the synchronization primitives. We believe that can significantly improve the quality and productivity of concurrent algorithms research and development and become the state-of-the-art tool for checking their correctness.

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A Concurrency-Optimal Binary Search Tree

Cited by 8 publications

References 26 publications

PaC-trees: Supporting Parallel and Compressed Purely-Functional Collections

PaC-trees: Supporting Parallel and Compressed Purely-Functional Collections

Optimal Concurrency for List-Based Sets

Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM

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