3 papers
cs.DC2026
GPU Memory and Utilization Estimation for Training-Aware Resource Management: Opportunities and Limitations
Ehsan Yousefzadeh-Asl-Miandoab, Reza Karimzadeh, Danyal Yorulmaz +2
Collocating deep learning training tasks improves GPU utilization but risks resource contention, severe slowdowns, and out-of-memory (OOM) failures. Accurate memory estimation is e…
cs.LG2025
TensorSocket: Shared Data Loading for Deep Learning Training
Ties Robroek, Neil Kim Nielsen, Pınar Tözün
Training deep learning models is a repetitive and resource-intensive process. Data scientists often train several models before landing on a set of parameters (e.g., hyper-paramete…
eess.IV2025
Adaptive and Robust Image Processing on CubeSats
Robert Bayer, Julian Priest, Daniel Kjellberg +5
CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility…