Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs

2021/01/27 by G. B. Cerati, P. Elmer, Cerati, Giuseppe +31
Medicine · Physics and Astronomy · #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Parallel #Particle Detector Development and Performance #Radiation Detection and Scintillator Technologies #Radiation Therapy and Dosimetry #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2101.11489

openalex publication_date 2021/01/27 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28

Abstract

We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.

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