Prospects of GPGPU in the Auger Offline Software Framework

TL;DR

This paper investigates leveraging GPGPU technology to enhance the computational efficiency of the Offline software framework used in the Pierre Auger Observatory, focusing on radio data analysis and parallelization strategies.

Contribution

It systematically profiles the Offline framework to identify GPU-parallelizable code sections and discusses strategies and challenges for GPGPU integration in existing experimental software.

Findings

Identification of code areas suitable for GPU parallelization.

Analysis of potential speed-ups through GPGPU implementation.

Discussion of obstacles in adapting existing frameworks for GPU use.

Abstract

The Pierre Auger Observatory is the currently largest experiment dedicated to unveil the nature and origin of the highest energetic cosmic rays. The software framework 'Offline' has been developed by the Pierre Auger Collaboration for joint analysis of data from different independent detector systems used in one observatory. While reconstruction modules are specific to the Pierre Auger Observatory components of the Offline framework are also used by other experiments. The software framework has recently been extended to incorporate data from the Auger Engineering Radio Array (AERA), the radio extension of the Pierre Auger Observatory. The reconstruction of the data of such radio detectors requires the repeated evaluation of complex antenna gain patterns which significantly increases the required computing resources in the joint analysis. In this contribution we explore the usability of massive parallelization of parts of the Offline code on the GPU. We present the result of a systematic profiling of the joint analysis of the Offline software framework aiming for the identification of code areas suitable for parallelization on GPUs. Possible strategies and obstacles for the usage of GPGPU in an existing experiment framework are discussed.