[Help needed] Training RNN models on GPU with high CPU usage

I am training some models on GPU, however, it seems like the current (runtime) performance is limited by CPUs. I got something like %Cpu(s): 29.3 us, 54.2 sy, ... in top. Is there any common reason for this?

Any thoughts for this will be appreciated

What’s your current hardware configuration? [CPU, GPU]

16 core E5 CPUs with a GTX 1080 Ti GPU; GPU utilization is around 30%

Do you mind providing some more information about your model and task?

I think it may be hard to diagnose the issue without it. My initial thought is that some processing task each time is maxing out one of the CPU threads and is bottlenecking the model but I can’t say without knowing the model and task and how it’s implemented. I’m not sure if someone else has any other thoughts?

I am doing a RNN model. I got a customized dataloader, which takes a string and return the padded sequence and its original length (i.e., “abc” => [1,2,3,0,0], 3 when padding to length 5). And these data are feed into a lstm model with sequence packing.

Also, if it is a CPU job, shouldn’t the CPU util be around 100% with a small sys?

Hi Julian,
Do you have any further ideas for this?