Abstract

HIJING++ (Heavy Ion Jet INteraction Generator) is the successor of the widely used original HIJING, developed almost three decades ago. While the old versions (1.x and 2.x) were written in FORTRAN, HIJING++ was completely rewritten in C++. During the development we keep in mind the requirements of the high-energy heavy-ion community: the new Monte Carlo software have a well designed modular framework, therefore any future modifications are much easier to implement. It contains all the physical models that were also present in it’s predecessor, but utilizing modern C++ features it also includes native thread based parallelism, an easy-to-use analysis interface and a modular plugin system, which makes room for possible future improvements. In this paper we summarize the results of our performance tests measured on 2 widely used architectures.

Highlights

  • During the approaching Long Shutdown 2 (LS2) of the Large Hadron Collider (LHC) in 2019–2020 many technical improvement will occur in the accelerator complex, in the detector and in the data acquisition systems

  • The new HIJING++ heavy-ion Monte Carlo framework is written from scratch with a modular, effective

  • We found that HIJING++ is ∼30% faster than PYTHIA8.2 and

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Summary

Introduction

During the approaching Long Shutdown 2 (LS2) of the Large Hadron Collider (LHC) in 2019–2020 many technical improvement will occur in the accelerator complex, in the detector and in the data acquisition systems These will result in a huge increase of the number of expected collisions per second and the amount of measured data per event will grow rapidly. It is possible to replace any of the main components, such as the jet quenching and shadowing algorithms, in a convenient, well defined way, without modifying the core code An another built-in feature is the above mentioned HijAnalysis framework, which adds the possibility to define any kind of data collecting objects, such as ROOT TTrees, histograms or simple ASCII files to collect all final state particles event-by-event.

Benchmark Setups
Results
Summary and Conclusions

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