CLUE is a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster (Rovere et al. 2020). The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. It is currently used in the CMS and CLIC event reconstruction software for clustering calorimetric hits in two dimensions (x,y) based on their energy. CLUE is implemented in C++ and can execute on CPU and GPUs thanks to the Alpaka performance portability library. CLUE has been generalized to k-dimensions and has been wrapped as python library (CLUEstering) to become more beneficial for the scientific community. The current Python integration does not support Alpaka, and therefore GPU offloading can’t be exploited. For this reason having Alpaka integrated in the Python library would be extremely useful to add support to parallel backend and heterogeneous devices such as GPUs and FPGAs.
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