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Virtualization for Scientific Workload

机译：虚拟化科学工作负载

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The computationally intensive applications (HPC) and the data analytics within the Big Data applications (BDA) constitute a combined workflow for the scientific discovery. Yet, the development and the execution environment for the HPC and the BDA have different origins and also within a single discipline there are large differences for the requirements for the provisioning of the libraries and tools within the Linux Operating System. Traditionally the HPC library versioning is addressed with the software Modules and other multi-root building frameworks by the system of administration. This does not necessary provide for the compatibility between different systems where the programs should be running. Such compatibility is required for the effective sharing of the code between scientific institutions and deployment on the centralized Supercomputing resources (Centers of Collective Usage). The flexibility of the software build model and the flexibility of subsequent deployment in the target computer center may be provided by the Virtualization and/or Containerization of the software. The Virtualization framework addresses also the problem of the unification between the HPC and the BDA workflow for a common scientific discovery model. Therefore virtualization is chosen as the software basis of the recent cluster deployment at Skoltech computer center. In the framework of this deployment model we have investigated the implications for the program run time when deployed in a virtualized environment. For the multitude of the scientific goals no compromise on the machine performance and program scalability can be accepted by the users of the system. Therefore a measurement of these parameters was undertaken for a representative range of scientific applications and for a multitude of running environments including the usage of CPU, GPU and High Speed networks. In this article we report a joined work between the Skolkovo Institute of Technology (Skoltech) and the National Research Center (Kurchatov Institute) comprising this evaluation. We have established the build and running environment using several container technologies. The setup retains more than 95% of the performance compared to the bare metal run times. The conditions for the usage of the Nvidia GPGPU within the containers are described and we have tested a model of the load balancing by migration of the containers to a different node. The presented work paves the way to a more flexible deploy everywhere model of execution that will speed up the scientific discovery and improve the opportunity for science.
机译：大数据应用程序（BDA）中的计算密集型应用程序（HPC）和数据分析构成了科学发现的组合工作流。但是，HPC和BDA的开发和执行环境有不同的渊源，并且在同一领域内，在Linux操作系统中提供库和工具的要求也存在很大差异。传统上，管理系统使用软件模块和其他多根构建框架来解决HPC库版本控制问题。这不必提供程序应在其中运行的不同系统之间的兼容性。这种兼容性是在科研机构之间有效共享代码以及在集中式超级计算资源（集体使用中心）上进行部署所必需的。可以通过软件的虚拟化和/或容器化来提供软件构建模型的灵活性以及在目标计算机中心中后续部署的灵活性。虚拟化框架还解决了通用科学发现模型在HPC和BDA工作流程之间统一的问题。因此，虚拟化被选为Skoltech计算机中心最近集群部署的软件基础。在此部署模型的框架中，我们研究了在虚拟环境中部署时对程序运行时间的影响。对于众多的科学目标，系统用户都不能接受对机器性能和程序可伸缩性的妥协。因此，针对代表性的科学应用范围以及包括CPU，GPU和高速网络的使用在内的多种运行环境进行了这些参数的测量。在本文中，我们报告了Skolkovo理工学院（Skoltech）与国家研究中心（Kurchatov研究所）的联合评估工作。我们已经使用多种容器技术建立了构建和运行环境。与裸机运行时间相比，该设置保留了超过95％的性能。描述了在容器中使用Nvidia GPGPU的条件，并且我们已经通过将容器迁移到其他节点测试了负载平衡模型。提出的工作为在任何地方更灵活地部署执行模型铺平了道路，这将加快科学发现并增加科学机会。

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