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Hierarchical \(\mathsf{k}_{\mathsf{t}}\) jet clustering for parallel architectures. (English) Zbl 1383.81004
Summary: The reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately.
More granular parallelization of the \(\mathsf{k}_{\mathsf{t}}\) cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The \(\mathsf{k}_{\mathsf{t}}\) method allows to know the development of particles due to the collision of high-energy nucleus-nucleus.
The hierarchical clustering algorithms works on graphs, so the particle information used by the standard \(\mathsf{k}_{\mathsf{t}}\) algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit’s standard longitudinal invariant \(\mathsf{k}_{\mathsf{t}}\) implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be \(1.6\) times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a \(12\) times faster computing performance, also being scalable enough to efficiently run on GPUs.
81-05 Experimental work for problems pertaining to quantum theory
81Vxx Applications of quantum theory to specific physical systems
65Y10 Numerical algorithms for specific classes of architectures
65Y05 Parallel numerical computation
Full Text: DOI
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