Abstract
NVIDIA is defining a High-Performance Computing system architecture called Cloud Native Supercomputing to provide bare-metal system performance with security isolation and functional offload capabilities. Cloud Native Supercomputing delivers a cloud-based user experience in a way that maintains the performance and scalability that is uniquely delivered with supercomputing facilities. This new set of capabilities is being driven by the need to accommodate new scientific workflows that combine traditional simulation with experimental data from the edge and combine it with AI, data analytics and visualization frameworks in an integrated and even real-time fashion. These new workflows stress the system management, security and non-computational functions of traditional cloud or supercomputing facilities. Specifically, workflows that include data from untrusted (or non-local) sources, user experiences that range from Jupyter notebooks and interactive jobs to Gordon Bell-class capacity batch runs and I/O patterns that are unique to the emerging mix of in silico and live data sources. To achieve these objectives, we introduce a new architectural component called the Data Processing Unit (DPU), which in early embodiments is a system-on-a-chip (SoC) that includes an InfiniBand (IB) and Ethernet network adapter, programmable Arm cores, memory, PCI switches, and custom accelerators. The BlueField-1 and BlueField-2 devices are NVIDIA’s first DPU instances. This paper describes the architecture of cloud native supercomputing systems that use DPUs for isolation and acceleration, along with system services provided by that DPU. These services provide enhanced security through isolation, file-system management capabilities, monitoring, and the offloaded support for communication libraries.
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Shainer, G. et al. (2022). NVIDIA’s Cloud Native Supercomputing. In: Nichols, J., et al. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation. SMC 2021. Communications in Computer and Information Science, vol 1512. Springer, Cham. https://doi.org/10.1007/978-3-030-96498-6_20
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DOI: https://doi.org/10.1007/978-3-030-96498-6_20
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