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May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

traccc - a close to single-source track reconstruction demonstrator for CPU and GPU

May 8, 2023, 11:30 AM
Hampton Roads Ballroom VI (Norfolk Waterside Marriott)

Hampton Roads Ballroom VI

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 3 - Offline Computing Track 3 - Offline Computing


Krasznahorkay, Attila (CERN)


Despite recent advances in optimising the track reconstruction problem for high particle multiplicities in high energy physics experiments, it remains one of the most demanding reconstruction steps in regards to complexity and computing ressources. Several attemps have been made in the past to deploy suitable algorithms for track reconstruction on hardware accelerators, often by tailoring the algorithmic strategy to the hardware design. This led in certain cases to algorithmic compromises, and often came along with simplified descriptions of detector geometry, input data and magnetic field.
The traccc project is an R&D initiative of the ACTS common track reconstruction; it aims to provide a complete track reconstruction chain for both CPU and GPU architectures. Emphasis has been put on sharing as much common source code as possible while trying to avoid algorithmic and physics performance compromises. Within traccc, dedicated components have been developed that are usable on standard CPU and GPU architectures: an astraction layer for linear algebra operations that allows to customize the mathematical backend (algebra-plugin), a host and device memory management system (vecmem), a generic vector field library (covfie) for the magneic field description, and a geometry and propagation library (detray). They serve as building blocks of a fully developed track reconstruction demonstrator based on clustering (connected component labelling), space point formation, track seeding and combinatorial track finding.
We present the concepts and implementation of the traccc demonstrator and classify the physics and computational performance on selected hardware using the Open Data Detector in an scenario minicking the HL-LHC run condition. In addition, we give insight in our attempts to use different native language and portability solutions for GPUs, and summarize our main findings during the development of the entire traccc project.

Consider for long presentation Yes

Primary authors

Salzburger, Andreas (CERN) Joana, Niermann (University of Goettingen) Krasznahorkay, Attila (CERN) Swatman, Stephen Nicholas (CERN) Yeo, Beomki (Lawrence Berkeley National Laboratory) Metelo Rita De Almeida, Guilherme (University of Lisbon)

Presentation materials