利用分布式系统中的轨迹并行化计算离子-分子碰撞截面

IF 3.4 3区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Parallel and Distributed Computing Pub Date : 2024-04-22 DOI:10.1016/j.jpdc.2024.104902
Samuel Cajahuaringa , Leandro N. Zanotto , Sandro Rigo , Hervé Yviquel , Munir S. Skaf , Guido Araujo
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引用次数: 0

摘要

离子迁移质谱法(IM-MS)是分析复杂分子结构特征的一种强有力的分析方法。在 IM-MS 中,被测样品被电离并在电场的推动下进入漂移管,与缓冲气体发生碰撞。然后,通过旋转平均碰撞截面(CCS)值的差异来测量离子气相的分离情况。测量的碰撞截面(CCS)对结构表征的有效性主要取决于理论计算的验证。这一验证过程依赖于密集的分子力学模拟,而分子力学模拟对计算要求很高,尤其是对于分子组装和病毒等大型系统。因此,需要可靠、快速的 CCS 计算来帮助解释 IM-MS 实验数据。这项工作介绍了 MassCCS 软件,该软件通过实施基于链接单元的算法,结合高性能计算(HPC)技术,大大提高了 CCS 模拟性能。我们对系统的大小、形状和 CPU 内核数量进行了大量测试。实验结果表明,与离子迁移谱碰撞模拟器(CoSIMS)和高性能碰撞截面(HPCCS)相比,在单节点执行时,速度最多可提高 3 个数量级。此外,我们还采用 OpenMP Cluster(OMPC)在节点间扩展了 MassCCS。OMPC 是一种创新的编程模型,专为开发 HPC 应用程序而设计。它仅使用 OpenMP 指令就能简化开发流程和软件维护。值得注意的是,OMPC 的性能可与纯 MPI 实现相媲美。这一改进使得使用氮缓冲气进行昂贵的 CCS 计算成为可能,对大型系统(如拥有 1,100 万个原子的人类腺病毒)的计算仅需 4 分钟,从而使 MassCCS 成为我们目前所知性能最好的软件。MassCCS 可在 https://github.com/cces-cepid/massccs 免费提供给学术界使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Ion-molecule collision cross-section calculations using trajectory parallelization in distributed systems

Ion Mobility coupled with Mass Spectrometry (IM-MS) stands as a strong analytical method for structurally characterizing complex molecules. In IM-MS, the sample under investigation is ionized and propelled by an electric field into a drift tube, which collides against a buffer gas. The separation of the ion gas phase is then measured through the differences in their rotationally averaged Collision Cross-Section (CCS) values. The effectiveness of the measured Collision Cross-Section (CCS) for structural characterization critically depends on the validation against theoretical calculations. This validation process relies on intensive molecular mechanics simulations, which can be computationally demanding, especially for large systems such as molecular assemblies and viruses. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS experimental data. This work presents the MassCCS software, which considerably increases the CCS simulation performance by implementing a linked-cell-based algorithm, incorporating High-Performance Computing (HPC) techniques. We performed extensive tests regarding the system size, shape, and number of CPU cores. Experimental results reveal speedups up to 3 orders of magnitude faster than Collision Simulator for Ion Mobility Spectrometry (CoSIMS) and High-Performance Collision Cross Section (HPCCS), optimized solutions for CCS simulations, for a single node execution. In addition, we extended MassCCS at the inter-node level by employing OpenMP Cluster (OMPC). OMPC is an innovative programming model designed for the development of HPC applications. It streamlines the development process and simplifies software maintenance using only OpenMP directives. Notably, OMPC delivers a performance level comparable to a pure MPI implementation. This enhancement enabled expensive CCS calculations using nitrogen buffer gas for large systems such as human adenovirus with ∼11 million atoms in just ∼4 min, making MassCCS the most performant software nowadays, to the best of our knowledge. MassCCS is available as free software for Academic use at https://github.com/cces-cepid/massccs.

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来源期刊
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing 工程技术-计算机:理论方法
CiteScore
10.30
自引率
2.60%
发文量
172
审稿时长
12 months
期刊介绍: This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing. The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.
期刊最新文献
SpEpistasis: A sparse approach for three-way epistasis detection Robust and Scalable Federated Learning Framework for Client Data Heterogeneity Based on Optimal Clustering Editorial Board Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) Survey of federated learning in intrusion detection
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