I started to lean the deep leaning by using Nvidia’s GPU.
At first ,I planned to analayze GPU internal operations of various Deep learning Frame works.
I could not analyze GPU internal operation by Tensorflow’s Visual profile.
Could you gove me nay advices?
Please refer below description about timeline. The result depends on the sample you profiled.
Process
A timeline will contain a Process row for each application profiled. The process identifier represents the pid of the process. The timeline row for a process does not contain any intervals of activity. Threads within the process are shown as children of the process.
Thread
A timeline will contain a Thread row for each CPU thread in the profiled application that performed either a CUDA driver or CUDA runtime API call. The thread identifier is a unique id for that CPU thread. The timeline row for a thread is does not contain any intervals of activity.
But I can not understand perfectly the reason why a lot of teread of GPU appeared in the Tensorflow’s profile .
Do you know the very simple sample code which indicate a lot of threads in profile?
The sample code need not to be same profile as Tensorflow’s profile, only indicates some threads of GPU in profile.
I tried to serach some sample codes which indicate a lot of threads in profile by using Windows CUDA sample codes, I never got the profile whinc I wish.