<scp>MolAR</scp> : Memory‐Safe Library for Analysis of <scp>MD</scp> Simulations Written in Rust

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.

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Publication Details

Journal
Journal of Computational Chemistry
Published
2024-12-01
DOI
https://doi.org/10.1002/jcc.27536
Citations
9
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
4.40

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article

MolAR : Memory‐Safe Library for Analysis of MD Simulations Written in Rust

Semen Yesylevskyy
9 citations
Journal of Computational Chemistry
Parallel Computing and Optimization Techniques
4.40
article

MolAR : Memory‐Safe Library for Analysis of MD Simulations Written in Rust

Semen Yesylevskyy
article en
9 citations

Abstract

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.

Journal of Computational ChemistryVol. 46(1)
Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry (CZ), Palacký University Olomouc (CZ)
European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Parallel Computing and Optimization Techniques
4.40
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<scp>MolAR</scp> : Memory‐Safe Library for Analysis of <scp>MD</scp> Simulations Written in Rust — Semen Yesylevskyy · Journal of Computational Chemistry (2024) | TGRS Research Map | TGRS