GPU-based reconstruction and data compression at ALICE during LHC Run 3

David Rohr

doi:10.1051/epjconf/202024510005

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 tracking chain during the asynchronous reconstruction for the silicon Inner Tracking System (ITS) and Transition Radiation Detector (TRD). The software is written in a generic way, such that it can also run on processors on the WLCG with the same reconstruction output. We give an overview of the status and the current performance of the reconstruction and the data compression implementations on the GPU for the TPC and for the global reconstruction.

Highlights

ALICE (A Large Ion Collider Experiment [1]) is one of the four major experiments at the LHC (Large Hadron Collider) at CERN
During the second long LHC shutdown in 2019 and 2020, the LHC upgrade will provide a higher Pb–Pb collision rate, and ALICE will update many of its detectors and systems [2]
The main tracking detectors TPC (Time Projection Chamber) and ITS (Inner Tracking System) will be upgraded [3], and the computing scheme will change with the O2 online-offline computing upgrade [4]

Summary

Introduction

ALICE (A Large Ion Collider Experiment [1]) is one of the four major experiments at the LHC (Large Hadron Collider) at CERN. The synchronous processing will serve two main objectives: detector calibration and data compression. The output of the synchronous data processing will be compressed time frames, which are stored to an on-site disk buffer, and from there written to tapes. When the computing farm is not fully used for the synchronous processing, e.g. in periods without beam or during pp data taking, it will perform a part of the asynchronous reconstruction, which reprocesses the data and generates final reconstruction output. The part of asynchronous processing that exceeds the capacity of the farm will be done in the grid This asynchronous stage will employ the same algorithms and software as the synchronous stage, but with different settings, additional reconstruction steps, and final calibration.

GPU Reconstruction for the ALICE Central Barrel Detectors

Memory requirements and management

Findings

Conclusions