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cs.PF2025
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Marcin Chrapek, Marcin Copik, Etienne Mettaz +1
Large Language Models (LLMs) are increasingly deployed on converged Cloud and High-Performance Computing (HPC) infrastructure. However, as LLMs handle confidential inputs and are f…
cs.PF2025
Denoising Application Performance Models with Noise-Resilient Priors
Gustavo de Morais, Alexander Geiß, Alexandru Calotoiu +5
As parallel codes are scaled to larger computing systems, performance models play a crucial role in identifying potential bottlenecks. However, constructing these models analytical…