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Roland Pabel

Publications and source records attributed to Roland Pabel.

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RAMSES: Secure high-performance computing for sensitive data

Traditionally, the architecture of high-performance computing (HPC) systems is tailored for speed, while highly secure computer systems must sacrifice speed for security. However, a wide range of scientific domains, such as the life sciences, call for a combination of performance and security to allow processing sensitive data at scale. Here, we present RAMSES (Research Accelerator for Modeling and Simulation with Enhanced Security), an HPC system designed from the ground up to deliver high performance within a robust security framework. RAMSES integrates hardware-based memory encryption of AMD processors with state-of-the-art file encryption from IBM Storage Scale and the Thales CipherTrust manager, establishing an HPC platform that ensures continuous encryption throughout the data life cycle - at rest, in transit, and in use - in compliance with major data protection standards (European General Data Protection Regulation, ISO/IEC 27001 certification, and Federal Information Processing Standards). In addition, we implemented advanced operating system hardening, a multi-layered security architecture, and mandatory multi-factor authentication to adapt the HPC environment to increased security demands. Benchmark results from the biomedical sector demonstrate that the performance impact of the secure environment is limited and that integration of the conflicting requirements speed and security can be achieved while preserving a coherent, flexible, and user-friendly system.

cs.DC

Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM

Due to rising demands for Artificial Inteligence (AI) inference, especially in higher education, novel solutions utilising existing infrastructure are emerging. The utilisation of High-Performance Computing (HPC) has become a prevalent approach for the implementation of such solutions. However, the classical operating model of HPC does not adapt well to the requirements of synchronous, user-facing dynamic AI application workloads. In this paper, we propose our solution that serves LLMs by integrating vLLM, Slurm and Kubernetes on the supercomputer \textit{RAMSES}. The initial benchmark indicates that the proposed architecture scales efficiently for 100, 500 and 1000 concurrent requests, incurring only an overhead of approximately 500 ms in terms of end-to-end latency.

cs.DC