3 papers
cs.DB2024
Diba: A Re-configurable Stream Processor
Mohammadreza Najafi, Thamir M. Qadah, Mohammad Sadoghi +1
Stream processing acceleration is driven by the continuously increasing volume and velocity of data generated on the Web and the limitations of storage, computation, and power cons…
cs.DC2024
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Nikolai Merkel, Daniel Stoll, Ruben Mayer +1
Recently, graph neural networks (GNNs) have gained much attention as a growing area of deep learning capable of learning on graph-structured data. However, the computational and me…
cs.LG2024
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
Alexander Erben, Ruben Mayer, Hans-Arno Jacobsen
This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To…