Kubernetes容器编排作为灵活有效的科学数据分析框架

Anton Tesliuk, S. Bobkov, V. Ilyin, A. Novikov, A. Poyda, V. Velikhov
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引用次数: 7

摘要

本文介绍了Kubernetes基础设施中单粒子成像(SPI)实验数据分析管道的设计和部署细节。我们分析了不同有效载荷下的各种软件使用模式。流水线软件的组成部分包括传统的HPC(基于mpi的)应用程序、需要GPU计算的应用程序、基于gui的软件以及通过将数据分成几个独立的部分(数据并行)而可以自然并行化的软件。针对不同的有效载荷类型,提出了不同的部署方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kubernetes Container Orchestration as a Framework for Flexible and Effective Scientific Data Analysis
In this paper we present the design and deployment details for Single Particle Imaging (SPI) experiments data analysis pipeline in Kubernetes infrastructure. We have analyzed various software usage patterns for different payloads. Components of the pipeline software include traditional HPC (MPI-based) applications, applications which require GPU computations, GUI-based software and the software which can be parallelized naturally by dividing the data into several independent parts (Data parallelism). For every payload type individual deployment approach was proposed.
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