3 papers
cs.DC2026
A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Youssef Moawad, Fabian Lehmann +4
Scientific workflows are critical to scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution…
cs.PF2025
xMem: A CPU-Based Approach for Accurate Estimation of GPU Memory in Deep Learning Training Workloads
Jiabo Shi, Dimitrios Pezaros, Yehia Elkhatib
The global scarcity of GPUs necessitates more sophisticated strategies for Deep Learning jobs in shared cluster environments. Accurate estimation of how much GPU memory a job will…
cs.DC2025
Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters
Lauritz Thamsen, Yehia Elkhatib, Paul Harvey +3
Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data…