English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the High-Luminosity Large Hadron Collider (HL-LHC), one of the mostchallenging computational problems is expected to be finding and fittingcharged-particle tracks during event reconstruction. The methods currently inuse at the LHC are based on the Kalman filter. Such methods have shown to berobust and to provide good physics performance, both in the trigger andoffline. In order to improve computational performance, we exploredKalman-filter-based methods for track finding and fitting, adapted formany-core SIMD and SIMT architectures. Our adapted Kalman-filter-based softwarehas obtained significant parallel speedups using such processors, e.g., IntelXeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIAGPUs. Recently, an effort has started towards the integration of our softwareinto the CMS software framework, in view of its exploitation for the Run III ofthe LHC. Prior reports have shown that our software allows in fact for somesignificant improvements over the existing framework in terms of computationalperformance with comparable physics performance, even when applied to realisticdetector configurations and event complexity. Here, we demonstrate that in suchconditions physics performance can be further improved with respect to ourprior reports, while retaining the improvements in computational performance,by making use of the knowledge of the detector and its geometry.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector