AIIO / Graph Neural Network

[AIIO] ( revolutionizes the way for users to automatically tune the I/O performance of applications on HPC systems. It currently works on linear regression models but has more opportunities to work on heterogeneous data, such as programming info. This requires extending the linear regression model to more complex models, such as heterogeneous graph neural networks. The proposed work will include developing the graph neural work-based model to predict the I/O performance and interpretation.

AIIO / Graph Neural Network

  • Topics: AIIO/Graph Neural Network`
  • Skills: Python, Github, Machine Learning
  • Difficulty: Difficult
  • Size: Large (350 hours)
  • Mentor: Bin Dong, Suren Byna

The Specific tasks of the project include:

  • Develop the data pre-processing pipeline to convert I/O logs into formats which are required by the Graph Neural Network
  • Build and test the Graph Neural Network to model the I/O performance for HPC applications.
  • Test and evaluate the accuracy of the Graph Neural Network with test cases from AIIO
Bin Dong
Bin Dong
Research Scientist, Lawrence Berkeley National Laboratory

Bin’s research interests are in high-performance computing + big data + AI/non-AI.