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Workshop: Machine Learning with Graphs in High-Performance Computing Environments
DescriptionThe intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for learning graph data. This workshop will focus on the greatest challenges in utilizing high-performance computing (HPC) for machine learning with graphs and methods for exploiting extreme scale parallelism for data, computation, and model optimization. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning with graphs and to share the wide range of applications that would benefit from HPC-powered machine learning with graphs.
Event TypeWorkshop
TimeSunday, 17 November 20249am - 5:30pm EST
LocationB314
Tags
Artificial Intelligence/Machine Learning
Graph Algorithms
Scalable Data Mining
Registration Categories
W
Presentations
9:00am - 10:00am ESTInvited Talk: Graphs in the LLM Era: Enabling Effective and Efficient LLM Ecosystems
Presenter
10:00am - 10:30am ESTMorning Break
10:30am - 11:00am ESTMDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training
11:00am - 11:30am ESTAcceleration of Graph Neural Networks with Heterogenous Accelerators Architecture
11:30am - 12:30pm ESTInvited Talk: Advancing Graph AI: Tackling Efficiency, Application, and Explainability
Presenter
12:30pm - 2:00pm ESTLunch Break
2:00pm - 3:00pm ESTInvited Talk: Rethinking graph analytics benchmarks with the AGILE workflow
3:00pm - 3:30pm ESTAfternoon Break
3:30pm - 4:00pm ESTIRIS-GNN: Leveraging Graph Neural Networks for Scheduling on Truly Heterogeneous Runtime Systems
4:00pm - 4:30pm ESTScalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
4:30pm - 5:00pm ESTAM-DGCNN: Leveraging Graph Attention Networks and Edge Attributes for Link Classification in Knowledge Graphs
Author/Presenters
5:00pm - 5:30pm ESTClosing remarks
Presenter