Global Track Reconstruction and Data Compression Strategy in ALICE for LHC Run 3

TL;DR

This paper discusses a comprehensive strategy for real-time data reconstruction and compression in the ALICE experiment during LHC Run 3, enabling high-rate data processing and storage with GPU acceleration.

Contribution

It introduces a novel global tracking and data compression approach that leverages GPU acceleration to handle increased data rates and complexity in real-time during LHC Run 3.

Findings

Achieved a 20x data compression factor for TPC data.

Matched Run 2 offline tracking performance in online reconstruction.

Shifted computational load towards GPUs for enhanced processing efficiency.

Abstract

In LHC Run 3, ALICE will increase the data taking rate significantly, from an approximately 1 kHz trigger readout in minimum-bias Pb--Pb collisions to a 50 kHz continuous readout rate. The reconstruction strategy of the online-offline computing upgrade foresees a synchronous online reconstruction stage during data taking, which generates the detector calibration, and a posterior calibrated asynchronous reconstruction stage. The huge amount of data requires a significant compression in order to store all recorded events. The aim is a factor 20 compression of the TPC data, which is one of the main challenges during synchronous reconstruction. In addition, the reconstruction will run online, processing 50 times more collisions than at present, yielding results comparable to current offline reconstruction. These requirements pose new challenges for the tracking, including the continuous TPC readout, more overlapping collisions, no a priori knowledge of the primary vertex position and of location-dependent calibration during the synchronous phase, identification of low-momentum looping tracks, and a distorted refit to improve track model entropy coding. At the 2018 workshop, the TPC tracking for Run 3 was presented, which matches the physics performance of the Run 2 offline tracking. It leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction stages. Porting more reconstruction steps like the remainder of the TPC reconstruction and tracking for other detectors to GPU will shift the computing balance from traditional processors towards GPUs. These proceedings focus on the global tracking strategy, including the ITS and TRD detectors, offloading more reconstruction steps onto GPU, and the approaches taken to achieve the necessary data compression.