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谷歌加速生物分子模拟

Google-Accelerated Biomolecular Simulations.

作者信息

Kohlhoff Kai J

机构信息

Research, Google, Mountain View, CA, USA.

出版信息

Methods Mol Biol. 2019;2022:291-309. doi: 10.1007/978-1-4939-9608-7_12.

Abstract

Biomolecular simulations rely heavily on the availability of suitable compute infrastructure for data-driven tasks like modeling, sampling, and analysis. These resources are typically available on a per-lab and per-facility basis, or through dedicated national supercomputing centers. In recent years, cloud computing has emerged as an alternative by offering an abundance of on-demand, specialist-maintained resources that enable efficiency and increased turnaround through rapid scaling.Scientific computations that take the shape of parallel workloads using large datasets are commonplace, making them ideal candidates for distributed computing in the cloud. Recent developments have greatly simplified the task for the experimenter to configure the cloud for use and job submission. This chapter will show how to use Google's Cloud Platform for biomolecular simulations by example of the molecular dynamics package GROningen MAchine for Chemical Simulations (GROMACS). The instructions readily transfer to a large variety of other tasks, allowing the reader to use the cloud for their specific purposes.Importantly, by using Docker containers, a popular light-weight virtualization solution, and cloud storage, key issues in scientific research are addressed: reproducibility of results, record keeping, and the possibility for other researchers to obtain copies and directly build upon previous work for further experimentation and hypothesis testing.

摘要

生物分子模拟在很大程度上依赖于适用于数据驱动任务(如建模、采样和分析)的合适计算基础设施。这些资源通常按实验室和设施提供,或通过专门的国家超级计算中心获取。近年来,云计算作为一种替代方案出现,它提供了大量按需提供、由专业人员维护的资源,通过快速扩展实现了高效性并缩短了周转时间。使用大型数据集的并行工作负载形式的科学计算很常见,这使其成为云中分布式计算的理想候选对象。最近的发展极大地简化了实验人员配置云以进行使用和作业提交的任务。本章将以化学模拟分子动力学软件包(GROMACS)为例,展示如何使用谷歌云平台进行生物分子模拟。这些说明很容易转移到各种其他任务上,使读者能够根据自己的特定目的使用云。重要的是,通过使用流行的轻量级虚拟化解决方案Docker容器和云存储,解决了科学研究中的关键问题:结果的可重复性、记录保存,以及其他研究人员获取副本并直接在先前工作基础上进行进一步实验和假设检验的可能性。

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