Potential of the Julia programming language for high energy physics computing (2306.03675v2)

Abstract: Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified

• The paper demonstrates that Julia uniquely bridges high-level ease with C/C++ performance through just-in-time compilation.
• It details how Julia's multiple dispatch and seamless interoperability with established HEP libraries enhance code reuse and integration.
• Empirical benchmarks in CMS data analysis highlight Julia's efficiency, achieving significantly faster runtimes than Python.

The Potential of the Julia Programming Language for High Energy Physics Computing

The paper critically evaluates the suitability of the Julia programming language for high energy physics (HEP) computing. Given the computational requirements of HEP research, including resource-intensive data processing and the necessity for efficient code that can be easily developed and maintained, the choice of programming language is pivotal.

Key Aspects of Julia for HEP

1. Performance and Ease of Use: Julia offers a unique combination of high-level programming ease and high-performance computing capability. Its design addresses the "two-language problem" prevalent in HEP and other scientific fields, where researchers typically use Python for ease of development and C++ for performance-critical tasks. Julia’s just-in-time (JIT) compilation enables performance comparable to C/C++, as demonstrated in microbenchmarks and applied examples like the CMS data analysis scenario.
2. Polymorphism and Code Reusability: Julia leverages dynamic multiple dispatch, enhancing code reuse by easily integrating functions across different packages without prior compatibility considerations. This paradigm supports more flexible and adaptable code, reducing overhead associated with rigid object-oriented designs typical in C++.
3. Interoperability: Julia ensures seamless integration with existing HEP software libraries written in C, C++, and Fortran. The interoperability extends to dynamic languages like Python, facilitated by packages such as PyCall.jl, enabling a smooth transition of workflows and existing investment in software infrastructure.
4. Package Management and Reproducibility: The built-in Pkg.jl package manager provides robust dependency management and environment reproducibility, ensuring consistent computational environments across different systems. This is vital in collaborative HEP experiments with complex software dependencies.
5. Support for Parallel and Distributed Computing: Julia includes native support for multithreading and distributed computing, crucial for handling the massive data throughput in HEP. Out-of-core distributed computing is offered via packages like Dagger.jl, supporting the scalability required for large HEP datasets.
6. Comprehensive Ecosystem: Julia's ecosystem encompasses libraries for numerical optimization, automatic differentiation, symbolic mathematics, and machine learning, presenting a unified environment for diverse HEP computational tasks. Tools like Turing.jl for Bayesian inference and Symbolics.jl for symbolic computation further enrich Julia's applicability to theoretical and simulated aspects of HEP research.

Numerical Results and HEP Specific Requirements

The paper presents performance metrics where Julia demonstrates competitive, if not superior, runtime efficiency compared to established languages. In an empirical evaluation of a di-muon analysis using CMS data, Julia outperformed equivalent Python implementations by orders of magnitude in speed, highlighting its potential for data-intensive HEP applications.

Implications and Future Prospects

The implications of Julia's adoption in HEP are significant, potentially transforming software development practices by unifying high-level scripting with low-level execution. This can reduce time-to-insight in research, improve collaboration, and lower the barrier to effective high-performance computing use among physicists.

The authors propose that further development of Julia-specific foundational HEP libraries could facilitate its adoption. These would include comprehensive interfacing with ROOT, support for HEP data formats, and specialized statistical tools pertinent to the field, such as those for profile likelihood techniques used in confidence level determination.

Conclusion

The paper makes a compelling case for Julia as an advantageous language for HEP computing, citing its ability to streamline the development workflow without compromising performance. Julia's features already fulfill most requirements for HEP computing, suggesting that it is feasible and beneficial for the HEP community to consider large-scale Julia adoption with focused development on HEP-specific tooling. The continued growth of the Julia community in both academia and industry provides a supportive backdrop for this transition, ensuring long-term viability and innovation in the field.