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Virtualization for Scientific Workload

机译：虚拟化科学工作量

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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）内的计算密集型应用（HPC）和数据分析构成了科学发现的组合工作流程。然而，HPC和BDA的开发和执行环境具有不同的起源，并且在单个学科中也有很大的差异，对Linux操作系统内的库和工具提供的要求有很大差异。传统上，HPC库版本控制通过管理系统的软件模块和其他多根构建框架来解决。这无需提供程序之间应运行的不同系统之间的兼容性。这些兼容性是有效分享科学机构与集中超级计算资源（集体使用中心）之间的代码分担。软件构建模型的灵活性和随后部署在目标计算机中心中的随后部署的灵活性可以由软件的虚拟化和/或容器化提供。虚拟化框架也解决了HPC与共同科学发现模型的HPC与BDA工作流程的问题。因此，虚拟化被选为SKOLTECH计算机中心最近群集部署的软件。在此部署模型的框架中，我们在部署在虚拟化环境中时，我们研究了对程序运行时的含义。对于众多的科学目标，没有对机器性能的妥协和程序可伸缩性，可以由系统的用户接受。因此，对这些参数的测量是针对各种科学应用的代表范围和多种运行环境的测量，包括使用CPU，GPU和高速网络的使用。在本文中，我们在Skolkovo技术研究所（Skoltech）和国家研究中心（Kurchatov Institute）之间举报了加入的工作，包括该评估。我们使用多个集装箱技术建立了构建和运行环境。与裸金属运行相比，该设置保留了超过95％的性能。描述了用于在容器内使用NVIDIA GPGPU的条件，并且我们通过将容器迁移到不同的节点来测试负载平衡的模型。所呈现的工作铺平了一个更灵活的部署到各地的执行模式，将加快科学发现并改善科学的机会。

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