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                  <contributors><authors><author><style font="default" size="100%">Kessler, Robert</style></author><author><style font="default" size="100%">Nieroda, Lech</style></author><author><style face="bold" font="default" size="100%">Volpert, Simon</style></author><author><style font="default" size="100%">Gräf, Moritz</style></author><author><style font="default" size="100%">Achter, Viktor</style></author><author><style font="default" size="100%">Hunhold, Laslo</style></author><author><style face="bold" font="default" size="100%">Wesner, Stefan</style></author></authors></contributors><titles><title><style font="default" size="100%">Evaluating Trusted Execution Environment Performance for Genome Sequence Alignment: An AMD SEV Case Study</style></title><secondary-title><style font="default" size="100%">Proceedings of the SC ’25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis</style></secondary-title></titles><periodical><full-title><style font="default" size="100%">Proceedings of the SC ’25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis</style></full-title></periodical><dates><year><style font="default" size="100%">2025-11</style></year></dates><electronic-resource-num><style font="default" size="100%">10.1145/3731599.3767558</style></electronic-resource-num><urls><related-urls><url><style font="default" size="100%">https://doi.org/10.1145/3731599.3767558</style></url></related-urls></urls><abstract><style font="default" size="100%">HPC systems are used for a variety of applications from different fields and user groups. These research areas include, among others, the life sciences and medicine, engineering, the humanities, and the natural sciences. Applications from these areas all have different characteristics and computational demands. An important requirement is the security and protection of data, especially when dealing with highly sensitive data such as the human genome. In order to facilitate the processing of actual patient data on HPC systems, it is imperative to implement robust protective measures, particularly given the prevalence of multi-tenancy models in such systems. In this paper, we focus in particular on in-memory encryption with AMD SEV and SME. We compare two micro benchmarks and the BWA-MEM2 algorithm as genome sequencing workflow against each other. Our evaluation matrix includes a SMP node, a VM with SEV and SME enabled and VM with only SME enabled, all assessed against varying thread counts and different file systems for the input data. Our analysis showed that the location of the input data has a minor impact on performance, while the encrypted file system causes the most overhead. Memory bandwidth seems to be the limiting factor as the bandwidth drops by approximately 50% with SEV and SME being enabled. Overall we observed that the overhead caused by the encryption for the genome alignment workload is adequate with 10.4% for SME and over 20.9% for SEV+SME using 32 threads, but the algorithm scales worse for larger thread counts compared to the native execution.</style></abstract></record>
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                  <contributors><authors><author><style font="default" size="100%">Kessler, Robert</style></author><author><style face="bold" font="default" size="100%">Volpert, Simon</style></author><author><style face="bold" font="default" size="100%">Wesner, Stefan</style></author></authors></contributors><titles><title><style font="default" size="100%">Towards Improving Resource Allocation for Multi-Tenant HPC Systems: An Exploratory HPC Cluster Utilization Case Study</style></title><secondary-title><style font="default" size="100%">2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)</style></secondary-title></titles><periodical><full-title><style font="default" size="100%">2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)</style></full-title></periodical><dates><year><style font="default" size="100%">2024-09</style></year></dates><electronic-resource-num><style font="default" size="100%">10.1109/CLUSTERWorkshops61563.2024.00019</style></electronic-resource-num><urls><related-urls><url><style font="default" size="100%">https://doi.org/10.1109/CLUSTERWorkshops61563.2024.00019</style></url></related-urls></urls><abstract><style font="default" size="100%">On-premise HPC systems are usually operated in such a way that the resources requested by its users are allocated exclusively to the corresponding jobs for the duration of their runtime. In order to guarantee the high availability of the resources and to minimize mutual interference due to the noisy-neighbor effect, resources are generally not overbooked, although this may lead to an under-utilization. In this context, we distinguish between two types of under-utilization: those relating to the allocation and those relating to the resources itself. Allocation under-utilization is a circumstance where not all available resources are being allocated, despite the fact that jobs are queued, due to potential cluster policies or bad scheduling. Resource under-utilization thereby mainly arises from bad performing code or due to task dependencies and is caused by the user. For both types of under-utilization, we define methodologies to determine under-utilization at the node and thus also at the cluster level. We apply our methodology to the MIT Supercloud dataset and evaluate it in terms of its cluster-wide utilization, due to the lack of specific data about resource allocation at the node level. We observed recurring longer phases of under-utilization in terms of both the allocation and the actual physical resource usage of the compute cores. In particular, within a 3-day time window around the peak allocation utilization of the overall cluster, we found a rather large disparity between the allocation and utilization of the computational resources of 14.7% with respect to the total available resource or 46.8% relative to the available allocated resources. We see major deficiencies with regard to the sustainable operation of such clusters, both in terms of the allocation under-utilization and the disparity between the actual resource utilization and the available allocated resources.</style></abstract></record>
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                  <contributors><authors><author><style font="default" size="100%">Trappen, Tim</style></author><author><style font="default" size="100%">Kessler, Robert</style></author><author><style font="default" size="100%">Pabel, Roland</style></author><author><style font="default" size="100%">Achter, Viktor</style></author><author><style face="bold" font="default" size="100%">Wesner, Stefan</style></author></authors></contributors><titles><title><style font="default" size="100%">Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM</style></title><secondary-title><style font="default" size="100%">Proceedings of the 1st International Workshop on Next-Gen Middleware for MLOps in Distributed Systems</style></secondary-title></titles><periodical><full-title><style font="default" size="100%">Proceedings of the 1st International Workshop on Next-Gen Middleware for MLOps in Distributed Systems</style></full-title></periodical><dates><year><style font="default" size="100%">2025-12</style></year></dates><electronic-resource-num><style font="default" size="100%">10.1145/3774902.3776632</style></electronic-resource-num><urls><related-urls><url><style font="default" size="100%">https://doi.org/10.1145/3774902.3776632</style></url></related-urls></urls><abstract><style font="default" size="100%">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 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.</style></abstract></record>
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                  <contributors><authors><author><style font="default" size="100%">Domaschka, Jörg</style></author><author><style face="bold" font="default" size="100%">Volpert, Simon</style></author><author><style font="default" size="100%">Maier, Kevin</style></author><author><style font="default" size="100%">Eisenhart, Georg</style></author><author><style font="default" size="100%">Seybold, Daniel</style></author></authors></contributors><titles><title><style font="default" size="100%">Using eBPF for Database Workload Tracing: An Explorative Study</style></title><secondary-title><style font="default" size="100%">Companion of the 2023 ACM/SPEC International Conference on Performance Engineering</style></secondary-title></titles><periodical><full-title><style font="default" size="100%">Companion of the 2023 ACM/SPEC International Conference on Performance Engineering</style></full-title></periodical><dates><year><style font="default" size="100%">2023</style></year></dates><electronic-resource-num><style font="default" size="100%">10.1145/3578245.3584313</style></electronic-resource-num><urls><related-urls><url><style font="default" size="100%">https://doi.org/10.1145/3578245.3584313</style></url></related-urls></urls></record>
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