Apr 20 – 30, 2020
Virtual/Digital only workshop
Europe/Paris timezone

Parallelizable Track Pattern Recognition in High-Luminosity LHC

Apr 20, 2020, 9:05 PM
25m
Virtual/Digital only workshop

Virtual/Digital only workshop

Speaker

Philip Chang (Univ. of California San Diego (US))

Description

The high instantaneous luminosity conditions in the High Luminosity Large Hadron Collider (HL-LHC) pose major computational challenges for the collider experiments. One of the most computationally challenging components is the reconstruction of charged-particle tracks. In order to efficiently operate under these conditions, it is crucial that we explore new and faster methods or implementations of charged-particle track reconstruction than what is being used today. Kalman-filter-based methods of the track pattern recognition that are currently used in the LHC experiments are inherently sequential and iterative and therefore cannot easily be accelerated through parallelization or vectorization by modern processors, such as graphics processing units (GPUs) or multicore processors. There have been attempts with great effort in vectorizing Kalman-filter-based methods of the track pattern recognition on modern processors with success. In this work, we instead start with a segment-linking-based algorithm that can be naturally parallelized and vectorized and is expected to run efficiently on modern processors. We established a preliminary segment-linking-based track pattern recognition for the CMS experiment using the Phase-II outer tracker and our findings and implications are presented here. This work is building on experience gained from a prototype of a similar approach studied in a different tracker layout geometry based on ideal detector simulation previously presented at CHEP2016.

Second most appropriate track (if necessary) Architectures and techniques for real-time tracking and fast track reconstruction
Consider for young scientist forum (Student or postdoc speaker) Yes

Primary authors

Philip Chang (Univ. of California San Diego (US)) Slava Krutelyov (Univ. of California San Diego (US)) Avi Yagil (Univ. of California San Diego (US)) Balaji Venkat Sathia Narayanan (Univ. of California San Diego (US)) Mario Masciovecchio (Univ. of California San Diego (US)) Matevz Tadel (Univ. of California San Diego (US))

Presentation materials

Peer reviewing

Paper