CppInterOp: Advancing Interactive C++ for High Energy Physics

The Cling C++ interpreter has transformed language bindings by enabling incremental compilation at runtime. This allows Python to interact with C++ on demand and lazily construct bindings between the two. The emergence of Clang-Repl, within the LLVM compiler framework, as a potential alternative to Cling highlights the need for a unified framework for interactive C++ technologies. We present CppInterOp, a C++ interoperability library that leverages Cling and LLVM’s Clang-Repl to provide a minimalist and backward-compatible API facilitating seamless language interoperability. This provides downstream interactive C++ tools with the compiler as a service by embedding Clang and LLVM as libraries in their codebases. By enabling dynamic Python interactions with static C++ codebases, CppInterOp enhances computational efficiency and rapid development in high-energy physics. The library offers reflection and ondemand JIT compilation APIs enabling cppyy, an automatic, run-time, Python- C++ bindings generator. We also demonstrate CppInterOp’s utility in diverse computing environments through its adoption as the runtime engine for xeuscpp, a Jupyter kernel designed for C++. CppInterOp is a general-purpose library inspired by the developments in the ROOT framework, which pushed the frontiers of interactive C++. It aims to extend this approach and serve as an integral component of ROOT, enhancing performance and resilience. This article introduces CppInterOp to the HEP community and showcases how it optimizes cross-language execution and computational tasks in high-energy physics, making it a valuable tool for researchers and developers.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-07
DOI
https://doi.org/10.1051/epjconf/202533701165/pdf
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

CppInterOp: Advancing Interactive C++ for High Energy Physics

Alexander Penev, Aaron Jomy, Baidyanath Kundu, Wim Lavrijsen et al.
Springer Link (Chiba Institute of Technology)
Parallel Computing and Optimization Techniques
article

CppInterOp: Advancing Interactive C++ for High Energy Physics

Alexander Penev, Aaron Jomy, Baidyanath Kundu, Wim Lavrijsen, Vassil Vassilev
article en

Abstract

The Cling C++ interpreter has transformed language bindings by enabling incremental compilation at runtime. This allows Python to interact with C++ on demand and lazily construct bindings between the two. The emergence of Clang-Repl, within the LLVM compiler framework, as a potential alternative to Cling highlights the need for a unified framework for interactive C++ technologies. We present CppInterOp, a C++ interoperability library that leverages Cling and LLVM’s Clang-Repl to provide a minimalist and backward-compatible API facilitating seamless language interoperability. This provides downstream interactive C++ tools with the compiler as a service by embedding Clang and LLVM as libraries in their codebases. By enabling dynamic Python interactions with static C++ codebases, CppInterOp enhances computational efficiency and rapid development in high-energy physics. The library offers reflection and ondemand JIT compilation APIs enabling cppyy, an automatic, run-time, Python- C++ bindings generator. We also demonstrate CppInterOp’s utility in diverse computing environments through its adoption as the runtime engine for xeuscpp, a Jupyter kernel designed for C++. CppInterOp is a general-purpose library inspired by the developments in the ROOT framework, which pushed the frontiers of interactive C++. It aims to extend this approach and serve as an integral component of ROOT, enhancing performance and resilience. This article introduces CppInterOp to the HEP community and showcases how it optimizes cross-language execution and computational tasks in high-energy physics, making it a valuable tool for researchers and developers.

Springer Link (Chiba Institute of Technology)
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Parallel Computing and Optimization Techniques
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