CLUEstering: a novel high-performance clustering library for scientific computing
Year: 2025
Type: article
Abstract: CLUEstering is a versatile clustering library based on CLUE, a density-based weighted clustering algorithm optimized for high-performance computing that supports clustering in an arbitraty. The library offers a user-friendly Python interface and a C++ backend to maximize performance. CLUE’s parallel design is tailored to exploit modern hardware accelerators, enabling it to process large-scale datasets with strong scalability and speed. To ensure performance portability across diverse architectures, the backend is implemented using alpaka, a C++ performance portability library that enables near-native performance on a wide range of accelerators with minimal code duplication. CLUEstering's unique combination of density-based and weighted clustering makes it a unique among popular clustering algorithms, many of which lack built-in support for such combination. This work will show comprehensive clustering results and performance benchmarks against other state-of-the-art algorithms. This work presents a new clustering library that combines density-based and weighted clustering, opening a new area of possibilities for clustering applications. The library is based on a highly parallel algorithm that supports clustering in an arbitrary number of dimensions and is implemented using a performance portability library that allows to leverage new types of accelerators with minimal code duplication.
DOI: 10.17181/eqzyp-6tj57
Authors: Balducci, Simone, Perego, Aurora
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Schmidt Family Foundation
OpenAlex ID: W7081966697
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