Published November 18, 2024
| Version v1
Software
Open
IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on truly Heterogeneous Runtime Systems
Contributors
Project leader:
-
1.
Oak Ridge National Laboratory
- 2. Canva Incorporated
Description
Digital Artefact for results presented in the paper: "IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on Truly Heterogeneous Runtime Systems" at Machine Learning with Graphs in High Performance Computing Environments (MLG-HPCE) Workshop as part of The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24).
IRIS is available for download from https://github.com/ORNL/iris while the digital artifact and archived results used to generate all figures in the paper can be found here.
If you have any questions or need help using IRIS-GNN please contact me directly at Beau Johnston <beau@inbeta.org>.
Files
README.md
Files
(164.3 MB)
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Additional details
Software
- Repository URL
- https://github.com/ORNL/iris
- Programming language
- C++ , C , Python
- Development Status
- Active