Transition to the memory safe natively compiled programming languages is a major software development trend in recent years, which eliminates memory‐related security exploits, enables a fearless concurrency and parallelization, and drastically improves ergonomics and speed of software development. Modern memory‐safe programing languages, such as Rust, are currently not used for developing molecular modeling and simulation software despite such obvious benefits as faster development cycle, better performance and smaller amount of bugs. This work introduces MolAR—the first memory‐safe library for analysis of MD simulations written in Rust. MolAR is intended to explore the advantages and challenges of implementing molecular analysis software in the memory‐safe natively compiled language and to develop specific memory‐safe abstractions for this kind of software. MolAR demonstrates an excellent performance in benchmarks outperforming popular molecular analysis libraries and tools, which makes it attractive for implementing computationally intensive analysis tasks. MolAR is freely available under Artistic License 2.0 at https://github.com/yesint/molar.

中文翻译：

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MolAR：用于分析用 Rust 编写的 MD 模拟的内存安全库


过渡到内存安全原生编译的编程语言是近年来的一个主要软件开发趋势，它消除了与内存相关的安全漏洞，实现了无所畏惧的并发和并行化，并大大改善了人体工程学和软件开发的速度。现代内存安全编程语言（如 Rust）目前尚未用于开发分子建模和模拟软件，尽管具有更快的开发周期、更好的性能和更少的错误等明显优势。这项工作介绍了 MolAR — 第一个用于分析 Rust 编写的 MD 模拟的内存安全库。MolAR 旨在探索在内存安全原生编译语言中实现分子分析软件的优势和挑战，并为此类软件开发特定的内存安全抽象。MolAR 在基准测试中表现出优于流行的分子分析库和工具的出色性能，这使得它对于实施计算密集型分析任务具有吸引力。MolAR 在 https://github.com/yesint/molar 的 Artistic License 2.0 下免费提供。