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Friday, January 06, 2023

Sampling, Pipelining Method Speeds Deep Learning on Large Graphs

Enhancing Neural Networks

Sampling, Pipelining Method Speeds Deep Learning on Large Graphs

MIT News, Lauren Hinkel, November 29, 2022

The SAmpling, sLIcing, and data movemeNT (SALIENT) methodology devised by Massachusetts Institute of Technology (MIT) and IBM Research scientists enhances graph neural networks (GNNs)' training and inference by clearing three bottlenecks in the computational pipeline. The researchers applied optimization to increase graphics processing unit (GPU) utilization in the PyTorch Geometric library for GNNs from 10% to 30%, improving performance up to double that of public benchmark codes. They addressed bottlenecks caused by graph sampling and mini-batch preparation algorithms at the beginning of the data pipeline by combining data structures and algorithmic optimizations, improving the sampling operation about threefold. MIT's Nickolas Stathas said SALIENT leveraged modern processors to further reduce per-epoch runtime via parallelizing feature slicing. ...  '

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