AI 中文总结
研究如何让Julia与现有HEP代码库互操作，通过静态编译JetReconstruction.jl包为例，对比Julia代码编译的两种后端，包括现有方法和Julia v1.12新特性，将其性能与原生Julia代码及C++ FastJet对比，为HEP社区探索Julia应用潜力。
AI 中文摘要
Julia编程语言被视为未来高能物理（HEP）计算的有力竞争者。向Julia生态系统过渡是个漫长过程，且需Julia与C++的互操作性。此前有将HEP C++包包装用于Julia的尝试，探索反向从现有主要用C++和Python编写的HEP代码库调用Julia代码很重要。随着Julia能生成静态编译代码，此方法渐可行。本文以静态编译JetReconstruction.jl包为例，比较了Julia代码编译的两种后端：现有方法和Julia v1.12版新的静态编译特性，并将其性能与原生Julia代码及C++ FastJet对比。
英文摘要
The Julia programming language is considered a strong contender as a future language for high-energy physics (HEP) computing. However, transitioning to the Julia ecosystem will be a long process and interoperability between Julia and C++ is required. So far several successful attempts have been made to wrap HEP C++ packages for use in Julia. It is also important to explore the reverse direction, allowing Julia code to be called from existing HEP codebases, written primarily in C++ and Python, which would significantly improve potential adoption of Julia code. With recent developments in Julia enabling it to produce statically compiled code, this approach is becoming increasingly feasible, and investigating this potential for the benefit of HEP community is the focus of this work. This work presents a case study of statically compiling the JetReconstruction.jl package - a highly performant, native Julia implementation of sequential jet reconstruction algorithms. Two different backends for Julia code compilation are compared: the existing PackageCompiler.jl and the new static compilation feature of Julia language, which is one of the major improvements in the Julia v1.12 release. The performance of the statically compiled JetReconstruction.jl is compared with both native Julia code and C++ FastJet.
