Hi, I am new to dgl and gnn.
When I run the graphSAGE example on the Reddit dataset(my GPU is Tesla T4), I found that DGL can add all training sets for training or inferencing, while pyG will be OOM when the batch size reaches about 9000.
I want to know what makes DGL memory use less than pyG?
ps: Is this benefit brought by ‘kernel fusion’?
I find the blog:https://www.dgl.ai/blog/2019/05/04/kernel.html
But I did not understand it clearly.