HEP-Frame: an Efficient Tool for Big Data Applications at the LHC

TL;DR

HEP-Frame is a C++ toolkit that enhances the efficiency of large-scale data analysis at the LHC by leveraging automatic resource evaluation and parallelism, significantly reducing execution times for complex physics datasets.

Contribution

This paper introduces HEP-Frame, a novel C++ package that automatically optimizes data analysis workflows for high-performance computing environments at the LHC.

Findings

Significant performance improvements in LHC data analyses.

Effective parallelization mechanisms based on hardware and code structure.

Successful case studies on top quark and Higgs boson analyses.

Abstract

HEP-Frame is a new C++ package designed to efficiently perform analyses of data sets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high performance servers and mini-clusters, and it was designed for natural science experts with a user-friendly interface to access structured databases. HEP-Frame automatically evaluates the underlying computing resources and builds an adequate code skeleton when creating a data analysis application. In run-time, HEP-Frame analyses a sequence of data sets exploring the available parallelism in the code and hardware resources: it concurrently reads inputs from an user-defined data structure and processes them, following the user specific sequence of requirements to select relevant data; it manages the efficient execution of that sequence; and it outputs results in user-defined objects (e.g., ROOT structures), stored together with the input data used. This paper shows how a domain expert software development can benefit from HEP-Frame, and how it significantly improved the performance of analyses of large data sets produced in proton-proton collisions at the LHC. Two case studies are discussed: the associated production of top quarks together with a Higgs boson (ttH) at the LHC, and a double and single top quark productions at the High-Luminosity phase of the LHC (HL-HLC). Results show that the HEP-Frame awareness of the analysis code behavior and structure, and the underlying hardware system, provides powerful and transparent parallelization mechanisms that largely improve the execution time of data analysis applications.