Containers improve the efficiency in application deployment and thus have been widely utilised on Cloud and lately in High Performance Computing (HPC) environments. Containers encapsulate complex programs with their dependencies in isolated environments making applications more compatible and portable. Often HPC systems have higher security levels compared to Cloud systems, which restrict users’ ability to customise environments. Therefore, containers on HPC need to include a heavy package of libraries making their size relatively large. These libraries usually are specifically optimised for the hardware, which compromises portability of containers.Per contra, a Cloud container has smaller volume and is more portable. Furthermore, containers would benefit from orchestrators that facilitate deployment and management of containers at a large scale. Cloud systems in practice usually incorporate sophisticated container orchestration mechanisms as opposed to HPC systems. Nevertheless, some solutions to enable container orchestration on HPC systems have been proposed in state of the art. This paper gives a survey and taxonomy of efforts in both containerisation and its orchestration strategies on HPC systems. It highlights differences thereof between Cloud and HPC. Lastly, challenges are discussed and the potentials for research and engineering are envisioned.

Containerisation demonstrates its efficiency in application deployment in cloud computing. Containers can encapsulate complex programs with their dependencies in isolated environments, hence are being adopted in HPC clusters. HPC workload managers lack micro-services support and deeply integrated container management, as opposed to container orchestrators (e.g. Kubernetes). We introduce Torque-Operator (a plugin) which serves as a bridge between HPC workload managers and container Orchestrators.

Remote-memory-access models, also known as one-sided communication models, are becoming an interesting alternative to traditional two-sided communication models in the field of High Performance Computing. In this paper we extend previous work on an MPI-based, locality-aware remote-memory-access model with a asynchronous progress-engine for non-blocking communication operations. Most previous related work suggests to drive progression on communication through an additional thread within the application process. In contrast, our scheme uses an arbitrary number of dedicated processes to drive asynchronous progression. Further, we describe a prototypical library implementation of our concepts, namely DART, which is used to quantitatively evaluate our design against a MPI-3 baseline reference. The evaluation consists of micro-benchmark to measure overlap of communication and computation and a scientific application kernel to assess total performance impact on realistic use-cases. Our benchmarks shows, that our asynchronous progression scheme can overlap computation and communication efficiently and lead to substantially shorter communication cost in real applications.