As graph data is a common language across science and engineering, growing machine learning models with graphs in HPC environments offer exciting opportunities.
The Workshop on Machine Learning with Graphs in High Performance Computing Environments will be held in conjunction with SC23: The International Conference for High Performance Computing, Networking, Storage and Analysis located in Denver, CO on November 12 - 17
Our keynote speakers will highlight significant research and challenges in machine learning with graphs in HPC.
Submit your paper on machine learning with graphs in HPC environments.
This workshop will feature presentations on accepted papers along with keynote speakers.
The workshop will be held in conjunction with SC23: The International Conference for High Performance Computing, Networking, Storage and Analysis located in Denver, CO on November 12 - 17. The 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.
Held in conjunction with SC23: The International Conference for High Performance Computing, Networking, Storage and Analysis
Mark your calendars for these dates!
August 11, 2023
Submission of workshop papers
September 8, 2023
Notification of paper acceptance
September 29, 2023
Camera-ready submission of accepted papers
November 13, 2023
Workshop