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Exploring the Performance of Singularity for High Performance Computing Scenarios

机译：探讨高性能计算场景的奇点性能

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Traditional hypervisor-based virtualization solutions have not been commonly used in High Performance Computing (HPC) due to the performance overhead. Container-based virtualization technologies represented by Linux Container and Docker can provide better resource sharing, customized environments and low overhead. However, they still can't satisfy the functional and security needs in HPC environments. Singularity is an attractive container-based approach to meet the requirements of scientific applications. Its characteristics such as mobility of compute, reproducibility, user freedom and support on existing traditional HPC, can effectively solve some flaws compared to other virtualization technologies. In this paper, we conducted a detailed performance evaluation of CPU, memory and network bandwidth between Singularity and bare metal by using HPL, STREAM and OSU Micro benchmarks. Four typical HPC applications such as NAMD, VASP, AMBER, and WRF are deployed to further validate the performance overhead of Singularity. Furthermore, migration and compatibility of Singularity are also investigated. Results from the experiments show that Singularity can achieve near-native performance in MPI and GPU parallel applications, which also demonstrate that it has a greater application prospect in HPC.
机译：由于性能开销，传统的虚拟机管理程序的虚拟化解决方案尚未在高性能计算（HPC）中使用。 Linux Container和Docker表示的基于集装箱的虚拟化技术可以提供更好的资源共享，自定义环境和低开销。但是，它们仍无法满足HPC环境中的功能和安全需求。奇点是一种有吸引力的基于集装箱方法，以满足科学应用的要求。它的特点如计算，再生性，用户自由和支持现有的传统HPC的迁移性，与其他虚拟化技术相比，可以有效地解决一些缺陷。在本文中，我们通过使用HPL，流和OSU微基准进行奇点和裸机之间的CPU，内存和网络带宽进行了详细的性能评估。部署了四种典型的HPC应用，例如NAMD，VASP，AMBER和WRF，以进一步验证奇点的性能开销。此外，还研究了互感的迁移和相容性。实验结果表明，奇点可以在MPI和GPU并联应用中实现近天然的性能，这也证明它具有更大的HPC应用前景。

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