But, if these users want to scale their research up to traditional HPC they may face a dead end. It can be used for some forms of local, private scientific computing as can be seen by researchers specifically utilizing their laptops and workstations for building these workflows and then pushing them into DockerHub. It is designed specifically for this use-case, and thus that is what it excels at. Starting with the Docker side of the question, Docker is a fantastic solution for micro-service virtualization for enterprise. While Docker is a container solution, there are multiple container solutions that are not Docker. Kurtzer: To answer accurately, I would have to clarify the question by separating Docker and containers. Is HPC suitable for containerized applications? Is there a particular category of HPC workloads that it’s better suited for?

InfoQ: You can see Docker and containers making inroads into all types of workloads. Singularity brings a new usage paradigm to the table which enables container support on HPC resources in such a manner that supports a much larger breadth of science use-cases as well as the focus on reproducibility and mobility of compute. This has worked fantastic for scientists who operate on local private resources, and have used technologies like Docker to enable collaboration and portability, but these technologies are significantly limited in terms of their applicability on general purpose HPC and scientific computing resources. Kurtzer: There is nothing specifically 'new' about using GPU capabilities inside containers, as technologies like 'nvidia-docker' have enabled that for some time now. What’s new about using GPU capabilities inside containers? InfoQ: GPUs have been around for a while. Kurtzer, a leader and contributor to many open source projects including Centos, Warewulf and Singularity about the evolution of the GPU tech conference and about containers and HPC. The topics were wide ranging from Virtual Reality to Containers, which have recently become a trend in the High Performance Computing arena. The conference had a main keynote, break out sessions, posters, and Hands-On Labs that were of interest to novices in GPUs, to deep learning experts alike. In his keynote address Jensen Huang, the CEO of NVIDIA, talked about the rebirth of AI, Deep Learning, and of course the next generation GPU codenamed Volta, and how that would address some of the pain points in training Machine Learning algorithms.

NVIDIA made a number of announcements at the recently concluded GPU Tech Conference.

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