用Rust编写的用于分析MD模拟的内存安全库

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Semen Yesylevskyy
{"title":"用Rust编写的用于分析MD模拟的内存安全库","authors":"Semen Yesylevskyy","doi":"10.1002/jcc.27536","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"46 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcc.27536","citationCount":"0","resultStr":"{\"title\":\"MolAR: Memory-Safe Library for Analysis of MD Simulations Written in Rust\",\"authors\":\"Semen Yesylevskyy\",\"doi\":\"10.1002/jcc.27536\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":188,\"journal\":{\"name\":\"Journal of Computational Chemistry\",\"volume\":\"46 1\",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcc.27536\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jcc.27536\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Chemistry","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcc.27536","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

向内存安全的本机编译编程语言过渡是近年来软件开发的主要趋势,它消除了与内存相关的安全漏洞,实现了无畏的并发和并行化,并大大提高了软件开发的人机工程学和速度。现代的内存安全编程语言,比如Rust,目前还没有被用于开发分子建模和仿真软件,尽管它们有更快的开发周期、更好的性能和更少的bug等明显的好处。本文介绍了molar——第一个用Rust编写的用于分析MD模拟的内存安全库。摩尔旨在探索在内存安全的本地编译语言中实现分子分析软件的优势和挑战,并为这种软件开发特定的内存安全抽象。mole在基准测试中表现出优异的性能,优于流行的分子分析库和工具,这使得它对实现计算密集型分析任务具有吸引力。根据艺术许可2.0,可以在https://github.com/yesint/molar上免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

MolAR: Memory-Safe Library for Analysis of MD 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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信