Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm

TL;DR

The paper presents a GPU-based, energy-efficient computing framework for ALICE's high data volume processing at CERN, integrating online reconstruction, data compression, and environmentally friendly cooling.

Contribution

It introduces a novel GPU-based event processing farm with integrated data compression and eco-friendly cooling, enhancing performance and sustainability for high-energy physics data processing.

Findings

Successful online data reconstruction using GPUs

Implementation of entropy-based data compression

Energy savings through adiabatic cooling system

Abstract

Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with PbPb collision data at a 50 kHz hadronic interaction rate. Also, the O$^2$ EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings.