First performance measurements with the Analysis Grand Challenge

TL;DR

This paper presents initial performance measurements of various analysis implementations within the IRIS-HEP Analysis Grand Challenge, focusing on their scalability and efficiency for HL-LHC data analysis workflows.

Contribution

It introduces a benchmark environment for HL-LHC analysis methods and compares different implementations using modern tools and infrastructure.

Findings

Different data delivery mechanisms impact performance

Caching strategies significantly affect analysis efficiency

Modern analysis tools can handle HL-LHC data workflows effectively

Abstract

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for comparing the usability and performance of different approaches and implementations. It includes all relevant workflow aspects from data delivery to statistical inference. The reference implementation for the AGC analysis task is heavily based on tools from the HEP Python ecosystem. It makes use of novel pieces of cyberinfrastructure and modern analysis facilities in order to address the data processing challenges of the HL-LHC. This contribution compares multiple different analysis implementations and studies their performance. Differences between the implementations include the use of multiple data delivery mechanisms and caching setups for the analysis facilities under investigation.