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

27 Jan 2021 · Giuseppe Cerati, Peter Elmer, Brian Gravelle, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio, Kevin McDermott, Boyana Norris, Allison Reinsvold Hall, Micheal Reid, Daniel Riley, Matevž Tadel, Peter Wittich, Bei Wang, Frank Würthwein, Avraham Yagil ·

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

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