A fast and efficient SIMD track reconstruction algorithm for the LHCb Upgrade 1 VELO-PIX detector

Abstract

The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We compare it to previous state-of-the-art scalar pattern recognition algorithms and show significantly faster processing and in some cases increased physics performance over all current alternatives. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance.

References in corpus (5)

• A Brief Introduction to PYTHIA 8.1
• LHCb Detector Performance
• Measurement of the track reconstruction efficiency at LHCb
• Allen: A high level trigger on GPUs for LHCb
• LHCb VELO Upgrade

Cited by in corpus (5)

• The LHCb upgrade I
• Fast Muon Tracking with Machine Learning Implemented in FPGA
• Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices
• Evolution of the energy efficiency of LHCb's real-time processing
• A parallel algorithm for fast reconstruction of primary vertices on heterogeneous architectures