Rusty-Cracker: A Multi-core Connected Components Library in Rust

We present Rusty-Cracker, a high-performance Rust library that implements a parallel version of the Cracker algorithm for efficiently identifying connected components in large-scale graphs. Designed to address the growing demands of graph analytics in fields such as social network analysis, bioinformatics, and infrastructure modeling, Rusty-Cracker leverages Rust's concurrency and memory safety features to ensure both speed and reliability. The adapted Cracker algorithm capitalizes on modern multi-core architectures through parallel processing, effectively minimizing synchronization overhead and optimizing workload distribution. This design significantly reduces computational time while maintaining accuracy. We present the implementation details and evaluate its performance on a real-world dataset of undirected graphs.

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Published
2025-07-20
DOI
https://doi.org/10.1145/3731545.3744334
Primary Topic
Algorithms and Data Compression
Type
article
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article

Rusty-Cracker: A Multi-core Connected Components Library in Rust

Davide Rucci, Emanuele Carlini, Matteo Mordacchini, Patrizio Dazzi et al.
Algorithms and Data Compression
article

Rusty-Cracker: A Multi-core Connected Components Library in Rust

Davide Rucci, Emanuele Carlini, Matteo Mordacchini, Patrizio Dazzi, Daniele Sampietro
article en

Abstract

We present Rusty-Cracker, a high-performance Rust library that implements a parallel version of the Cracker algorithm for efficiently identifying connected components in large-scale graphs. Designed to address the growing demands of graph analytics in fields such as social network analysis, bioinformatics, and infrastructure modeling, Rusty-Cracker leverages Rust's concurrency and memory safety features to ensure both speed and reliability. The adapted Cracker algorithm capitalizes on modern multi-core architectures through parallel processing, effectively minimizing synchronization overhead and optimizing workload distribution. This design significantly reduces computational time while maintaining accuracy. We present the implementation details and evaluate its performance on a real-world dataset of undirected graphs.

University of Pisa (IT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT), Institute of Informatics and Telematics (IT)
Openalex Percentile: Top 87%
Algorithms and Data Compression
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Rusty-Cracker: A Multi-core Connected Components Library in Rust — Davide Rucci, Emanuele Carlini, et al. · (2025) | TGRS Research Map | TGRS