11 citations · 16 across the 5 of their papers we have counts for
5 papers
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Gunduz Vehbi Demirci, Aparajita Haldar, Hakan Ferhatosmanoglu
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms…
Temporal Cascade Model for Analyzing Spread in Evolving Networks with Disease Monitoring Applications
Aparajita Haldar, Shuang Wang, Gunduz Demirci +2
Current approaches for modeling propagation in networks (e.g., spread of disease) are unable to adequately capture temporal properties of the data such as order and duration of evo…
Collective Shortest Paths for Minimizing Congestion on Temporal Load-Aware Road Networks
Chris Conlan, Teddy Cunningham, Gunduz Vehbi Demirci +1
Shortest path queries over graphs are usually considered as isolated tasks, where the goal is to return the shortest path for each individual query. In practice, however, such quer…
Partitioning sparse deep neural networks for scalable training and inference
Gunduz Vehbi Demirci, Hakan Ferhatosmanoglu
The state-of-the-art deep neural networks (DNNs) have significant computational and data management requirements. The size of both training data and models continue to increase. Sp…
Improving the Load Balancing Performance of Vlasiator
Ata Turk, Cevdet Aykanat, G. Vehbi Demirci +2
This whitepaper describes the load-balancing performance issues that are observed and tackled during the petascaling of the Vlasiator codes. Vlasiator is a Vlasov-hybrid simulation…