2 citations · 2 across the 6 of their papers we have counts for
6 papers
Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC
Ashna Nawar Ahmed, Banooqa Banday, Terry Jones +1
High-Performance Computing (HPC) schedulers must balance user performance with facility-wide resource constraints. The task boils down to selecting the optimal number of nodes for…
Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash +40
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Sc…
Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs
Mohammad Zaeed, Tanzima Z. Islam, Vladimir Inđić
Large Language Models (LLMs) show promise for automated code optimization but struggle without performance context. This work introduces Opal, a modular framework that connects per…
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
Matthias Maiterth, Wesley H. Brewer, Jaya S. Kuruvella +8
Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate schedulers are limited to post-deployment analysis, or simul…
Scalable GPU Performance Variability Analysis framework
Ankur Lahiry, Ayush Pokharel, Seth Ockerman +3
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insigh…
Novel Representation Learning Technique using Graphs for Performance Analytics
Tarek Ramadan, Ankur Lahiry, Tanzima Z. Islam
The performance analytics domain in High Performance Computing (HPC) uses tabular data to solve regression problems, such as predicting the execution time. Existing Machine Learnin…