Hi Mufeili, thanks for your answer.
Yes, i mean dgl\examples\pytorch\gcn (i tried also on gat and same observation).
By “torch experiments”, i mean an toy gcn model extracted from torch example on github that i launched one time on one GPU (gpu:0) and 2 time on (gpu:0) in //.
What’s the source of the expected execution time?
I compare the execution time between :
- One single training executed on GPU (gpu:0) : 250/250 [00:03<00:00, 76.79it/s]
- Two training executed on GPU (gpu:0) : 250/250 [00:05<00:00, 43.31it/s]; 250/250 [00:05<00:00, 43.95it/s]
By “2 or 3 times”, did you mean multi-GPU training? :
When i said 2 or 3 times, i meant that i did “python train.py --n-epoch 250” 3 time on the same GPU (gpu:0), so the model are independent and are executed in // on the same GPU (gpu:0). I measure the time with tqdm in the epoch loop.
So here my problem is : Why when i am executing 2 GCN training using DGL library on the same GPU, the execution time is twice as long as when I execute a single one while, with the same model (GCN) on pytorch, the execution time is the same if i launch 1 training or 2 training in // on the same GPU ?
Maybe it is something with cuda stream properties ? I read that is was not handle now by DGL ?
Thanks a lot