2 papers
cs.DC2026
Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
Jonas Svedas, Nathan Laubeuf, Ryan Harvey +6
Predicting the performance of large-scale distributed machine learning (ML) workloads across multiple accelerator architectures remains a central challenge in ML system design. Exi…
cs.DC2025
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf +6
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth i…