Speaker
Description
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.
Significance
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.
References
https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.591315/full
| Experiment context, if any | The work is related to the CMS experiment |
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