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CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics.

ABSTRACT: One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose 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. 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. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high-energy physics.

SUBMITTER: Rovere M

PROVIDER: S-EPMC8080903 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics.

Rovere Marco M Chen Ziheng Z Di Pilato Antonio A Pantaleo Felice F Seez Chris C

Frontiers in big data 20201127

One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the ave ...[more]

PMID: 33937749

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