Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs
A machine-learning technique called SALIENT addresses key bottlenecks in computation with graph neural networks by optimizing usage of the hardware, particularly GPUs. This upgrade significantly reduces training and inference time on extensive datasets to keep pace with fast-moving and growing data in finance, social networks, and fraud detection in cryptocurrency.
Source: mit.edu