GPU-based reconstruction and data compression at ALICE during LHC Run 3
David Rohr (for the ALICE Collaboration)

TL;DR
This paper discusses the implementation of GPU-based real-time data reconstruction and compression techniques for the ALICE experiment during LHC Run 3, addressing increased data rates and processing challenges.
Contribution
It introduces GPU-accelerated algorithms for online and offline reconstruction and data compression, enhancing processing speed and efficiency for high data rate environments.
Findings
GPU-based processing achieves real-time data reconstruction for TPC.
Significant data compression factors are attained with GPU algorithms.
Performance benchmarks show scalability and efficiency improvements.
Abstract
In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read out of minimum bias Pb-Pb collisions. The reconstruction strategy of the online offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration, and a posterior calibrated asynchronous reconstruction stage. The significant increase in the data rate poses challenges for online and offline reconstruction as well as for data compression. Compared to Run 2, the online farm must process 50 times more events per second and achieve a higher data compression factor. ALICE will rely on GPUs to perform real time processing and data compression of the Time Projection Chamber (TPC) detector in real time, the biggest contributor to the data rate. With GPUs available in the online farm, we are evaluating their usage also for the full…
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