Kubernetes with per-VNI Slingshot isolation is demonstrated on ARM64 Isambard-AI for a Trusted Research Environment and multi-node LLM serving, with ~0.9 us latency overhead.
Closing the HPC-Cloud Convergence Gap: Multi-Tenant Slingshot RDMA for Kubernetes
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abstract
Converged HPC-Cloud computing is an emerging computing paradigm that aims to support increasingly complex and multi-tenant scientific workflows. These systems require reconciliation of the isolation requirements of native cloud workloads and the performance demands of HPC applications. In this context, networking hardware is a critical boundary component: it is the conduit for high-throughput, low-latency communication and enables isolation across tenants. HPE Slingshot is a high-speed network interconnect that provides up to 200 Gbps of throughput per port and targets high-performance computing (HPC) systems. The Slingshot host software, including hardware drivers and network middleware libraries, is designed to meet HPC deployments, which predominantly use single-tenant access modes. Hence, the Slingshot stack is not suited for secure use in multi-tenant deployments, such as converged HPC-Cloud deployments. In this paper, we design and implement an extension to the Slingshot stack targeting converged deployments on the basis of Kubernetes. Our integration provides secure, container-granular, and multi-tenant access to Slingshot RDMA networking capabilities at minimal overhead.
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Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More
Kubernetes with per-VNI Slingshot isolation is demonstrated on ARM64 Isambard-AI for a Trusted Research Environment and multi-node LLM serving, with ~0.9 us latency overhead.