Created by: reyoung

We plan to tune fluid's performance with a loop with three steps:

1. Profile: To figure out which part of the fluid is slow.
2. Find problems & Give a fix: We will discuss and find the problems based on profile results.
3. Profile: To confirm the problems has been solved and the performance is improved.

There are several jobs for these three steps:

1. Find a machine with docker and GPU for profiling. @jacquesqiao
2. Neural network configurations for CNN, LSTM, etc. @qingqing01 @dzhwinter
   • https://github.com/PaddlePaddle/Paddle/pull/6056
3. Setup an environment for profiling. @chengduoZH
   • Use cProfile for Python, yap for Python/C++, nvprof for CUDA
4. Find problems: All members together.
5. Fix GPU problems: @jacquesqiao @qingqing01
6. Fix CPU problems: @dzhwinter
7. Fix Python problems: TODO