Bradley Rees

According to our database, Bradley Rees authored at least 14 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2025

SDT-GNN: Streaming-Based Distributed Training Framework for Graph Neural Networks.

Xin Huang, Weipeng Zhuo, Minh Phu Vuong, Shiju Li, Jongryool Kim, Bradley Rees, Chul-Ho Lee

Proceedings of the IEEE International Conference on Big Data, 2025

2024

CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks.

Xin Huang, Weipeng Zhuo, Minh Phu Vuong, Shiju Li, Jongryool Kim, Bradley Rees, Chul-Ho Lee

CoRR, 2024

2023

cuSLINK: Single-Linkage Agglomerative Clustering on the GPU.

Corey J. Nolet, Divye Gala, Alexandre Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing.

Seunghwa Kang, Chuck Hastings, Joe Eaton, Brad Rees

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

2022

GPU Semiring Primitives for Sparse Neighborhood Methods.

Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, Tim Oates

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow.

Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel, Dominique LaSalle

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass.

Xin Huang, Jongryool Kim, Bradley Rees, Chul-Ho Lee

Proceedings of the IEEE International Symposium on Workload Characterization, 2022

Analyzing Multi-trillion Edge Graphs on Large GPU Clusters: A Case Study with PageRank.

Seunghwa Kang, Joseph Nke, Brad Rees

Proceedings of the IEEE High Performance Extreme Computing Conference, 2022

RAPIDS cuGraph.

Alexandre Fender, Brad Rees, Joe Eaton

Proceedings of the Massive Graph Analytics, 2022

2021

Iterative Learning Control With Time-Delay Compensation: An Application to Twin-Roll Strip Casting.

Florian Browne, Brad Rees, George T.-C. Chiu, Neera Jain

IEEE Trans. Control. Syst. Technol., 2021

Semiring Primitives for Sparse Neighborhood Methods on the GPU.

Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, John Zedlewski, Tim Oates

CoRR, 2021

2020

Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS.

Bartley Richardson, Bradley Rees, Tom Drabas, Even Oldridge, David A. Bader, Rachel Allen

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters.

Seunghwa Kang, Alexandre Fender, Joe Eaton, Brad Rees

Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020

2013

Detecting insider threats in a real corporate database of computer usage activity.

Ted E. Senator, Henry G. Goldberg, Alex Memory, William T. Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A. Bader, Edmond Chow, Irfan A. Essa, Joshua Jones, Vinay Bettadapura, Duen Horng Chau, Oded Green, Oguz Kaya, Anita Zakrzewska, Erica Briscoe, Rudolph L. Mappus IV, Robert McColl, Lora Weiss, Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, Shubhomoy Das, Andrew Emmott, Jed Irvine, Jay Yoon Lee, Danai Koutra, Christos Faloutsos, Daniel D. Corkill, Lisa Friedland, Amanda Gentzel, David D. Jensen

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013