4 papers
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…
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…
Reaching the Edge of the Edge: Image Analysis in Space
Robert Bayer, Julian Priest, Pınar Tözün
Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ab…
An Analysis of Collocation on GPUs for Deep Learning Training
Ties Robroek, Ehsan Yousefzadeh-Asl-Miandoab, Pınar Tözün
Deep learning training is an expensive process that extensively uses GPUs, but not all model training saturates modern powerful GPUs. Multi-Instance GPU (MIG) is a new technology i…