Improvements of the ALICE GPU TPC tracking and GPU framework for online and offline processing of Run 3 Pb-Pb data
David Rohr (for the ALICE Collaboration)

TL;DR
This paper discusses recent improvements to the ALICE GPU-based TPC tracking framework, enhancing online and offline processing of high-rate Pb-Pb collision data at CERN's LHC, including algorithm refinements and performance optimizations using Runtime Compilation.
Contribution
The paper introduces new GPU tracking algorithms and framework optimizations, notably using Runtime Compilation, to improve processing efficiency for high-rate heavy-ion collision data.
Findings
Enhanced cluster attachment in inner TPC regions.
Successful real Pb-Pb data reconstruction with high occupancy.
Improved GPU framework performance and flexibility.
Abstract
ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz in LHC Run 3. To cope with such collision and data rates, ALICE uses a new GEM TPC with continuous readout and a GPU-based online computing farm for data compression. Operating the first GEM TPC of this size with large space charge distortions due to the high collision rate has many implications for the track reconstruction algorithm, both anticipated and unanticipated. With real Pb-Pb data available, the TPC tracking algorithm needed to be refined, particularly with respect to improved cluster attachment at the inner TPC region. In order to use the online computing farm efficiently for offline processing when there is no beam in the LHC, ALICE is currently running TPC tracking on GPUs also in offline processing. For the future, ALICE aims to run more computing steps…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Particle Detector Development and Performance · Particle physics theoretical and experimental studies
