Usage of GPUs in ALICE Online and Offline processing during LHC Run 3

TL;DR

This paper discusses how ALICE uses GPUs to handle increased data rates during LHC Run 3, focusing on both online and offline processing phases to improve data handling and processing efficiency.

Contribution

It presents the implementation and validation of GPU-based processing for ALICE's high data rate environment during LHC Run 3, including system testing and future prospects.

Findings

Successful GPU integration in the synchronous processing phase

Validation of GPU architecture through full system testing

Plans to extend GPU usage to asynchronous processing phase

Abstract

ALICE will significantly increase its Pb--Pb data taking rate from the 1\,kHz of triggered readout in Run 2 to 50 kHz of continuous readout for LHC Run 3. Updated tracking detectors are installed for Run 3 and a new two-phase computing strategy is employed. In the first synchronous phase during the data taking, the raw data is compressed for storage to an on-site disk buffer and the required data for the detector calibration is collected. In the second asynchronous phase the compressed raw data is reprocessed using the final calibration to produce the final reconstruction output. Traditional CPUs are unable to cope with the huge data rate and processing demands of the synchronous phase, therefore ALICE employs GPUs to speed up the processing. Since the online computing farm performs a part of the asynchronous processing when there is no beam in the LHC, ALICE plans to use the GPUs also for this second phase. This paper gives an overview of the GPU processing in the synchronous phase, the full system test to validate the reference GPU architecture, and the prospects for the GPU usage in the asynchronous phase.