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Related Experiment Video

Updated: Nov 7, 2025

Spatial Separation of Molecular Conformers and Clusters

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Published on: January 9, 2014

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

Marco Rovere¹, Ziheng Chen², Antonio Di Pilato^3,4

¹European Organization for Nuclear Research (CERN), Meyrin, Switzerland.

Frontiers in Big Data

|May 3, 2021

Summary

A new density-based clustering algorithm addresses high computing loads in high-granularity calorimeters for the High-Luminosity Large Hadron Collider upgrade. This parallelizable method efficiently clusters energy deposits, optimizing data processing for future physics experiments.

Keywords:

FCC HL-LHC calorimeters clustering density graphics processing unit high granularity

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

High-energy physics
Particle detector instrumentation
Computational physics

Background:

High granularity calorimeters, essential for experiments like the CMS Phase-2 Upgrade for the High-Luminosity Large Hadron Collider (HL-LHC), face significant computational challenges.
The large number of channels in these detectors leads to a surge in computing load during the reconstruction stage, specifically when clustering digitized energy deposits (hits).

Purpose of the Study:

To propose a novel, fast, and fully parallelizable density-based clustering algorithm.
To optimize this algorithm for high-occupancy scenarios typical in modern particle physics experiments.
To demonstrate its efficiency and scalability compared to existing methods.

Main Methods:

Developed a density-based clustering algorithm leveraging a grid spatial index for rapid neighbor querying.
Optimized the algorithm for high-occupancy environments where cluster counts exceed average hits per cluster.
Implemented and compared performance on both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) architectures.

Main Results:

The proposed algorithm exhibits linear scaling with the number of hits within the considered range, ensuring efficient processing.
Performance benchmarks show significant advantages of algorithmic parallelization, particularly on GPU implementations.
The algorithm is well-suited for high-occupancy scenarios, outperforming traditional methods in such conditions.

Conclusions:

The developed clustering algorithm offers a viable solution to the computational challenges posed by high granularity calorimeters.
Algorithmic parallelization, especially on heterogeneous computing platforms like GPUs, is crucial for future high-energy physics data reconstruction.
This work paves the way for more efficient data processing in upcoming high-luminosity particle physics experiments.