PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
Maurizio Rossi and Davide Brunelli
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
dataset 1
method 1
citation-polarity summary
years
2026 5representative citing papers
A systematic analysis of 59 quantum software testing empirical studies reveals highly diverse designs, inconsistent reporting, and open methodological challenges, leading to recommendations for future work.
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
Eidola is a gem5 extension that emulates cycle-level peer-to-peer GPU writes via real-application timing profiles to simulate traffic and synchronization in multi-GPU AI systems.
Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.
citing papers explorer
-
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
PI-DLinear integrates derived thermal ODEs into DLinear to forecast AI data center power more accurately than SOTA models while respecting physical constraints under throttling and transients.
-
Energy-Aware Metaheuristics
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
-
Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads
Eidola is a gem5 extension that emulates cycle-level peer-to-peer GPU writes via real-application timing profiles to simulate traffic and synchronization in multi-GPU AI systems.
-
Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions
Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.