A sensor characterization and reconstruction method enables fine-grained power attribution on Frontier and Portage exascale nodes, separating runtime and power effects in mixed-precision benchmarks.
Fine-grained application energy and power measurements on the frontier exascale system
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.DC 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A Kubernetes-based hybrid HPC-cloud platform on HPE Cray EX nodes is being developed at CSCS to enable fine-tuning pipelines and highly available inference services for foundation models within a batch-oriented supercomputing environment.
Apertus, a 70B open multilingual foundation model, was pre-trained on the Alps supercomputer, with details on adapting HPC infrastructure into a resilient ML platform.
citing papers explorer
-
Fine-Grained Power and Energy Attribution on AMD GPU/APU-Based Exascale Nodes
A sensor characterization and reconstruction method enables fine-grained power attribution on Frontier and Portage exascale nodes, separating runtime and power effects in mixed-precision benchmarks.
-
Beyond Pre-Training: The Full Lifecycle of Foundation Models on HPC Systems
A Kubernetes-based hybrid HPC-cloud platform on HPE Cray EX nodes is being developed at CSCS to enable fine-tuning pipelines and highly available inference services for foundation models within a batch-oriented supercomputing environment.
-
An Engineering Journey Training Large Language Models at Scale on Alps: The Apertus Experience
Apertus, a 70B open multilingual foundation model, was pre-trained on the Alps supercomputer, with details on adapting HPC infrastructure into a resilient ML platform.