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

Journal Article · Wed Jan 27 04:00:00 EST 2021 · TBD

OSTI ID:1823525

Cerati, Giuseppe ^[1]; Elmer, Peter; Gravelle, Brian; Kortelainen, Matti; Krutelyov, Vyacheslav; Lantz, Steven; Masciovecchio, Mario; McDermott, Kevin; Norris, Boyana; Reinsvold Hall, Allison; Reid, Micheal; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Wang, Bei; Wurthwein, Frank; Yagil, Avraham

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1. Fermilab

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.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1823525
• Report Number(s): FERMILAB-PUB-21-122-SCD; arXiv:2101.11489; oai:inspirehep.net:1842996
• Journal Information: TBD, Journal Name: TBD
• Country of Publication: United States
• Language: English

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS