Tune CPU performance

This tutorial introduces techniques we use to profile and tune the CPU performance of PaddlePaddle. We will use Python packages cProfile and yep, and Google’s perftools.

Profiling is the process that reveals performance bottlenecks, which could be very different from what’s in the developers’ mind. Performance tuning is done to fix these bottlenecks. Performance optimization repeats the steps of profiling and tuning alternatively.

PaddlePaddle users program AI applications by calling the Python API, which calls into libpaddle.so. written in C++. In this tutorial, we focus on the profiling and tuning of

  1. the Python code and

  2. the mixture of Python and C++ code.

Profiling the Python Code

Generate the Performance Profiling File

We can use Python standard package, cProfile, to generate Python profiling file. For example:

python -m cProfile -o profile.out main.py

where main.py is the program we are going to profile, -o specifies the output file. Without -o, cProfile would outputs to standard output.

Look into the Profiling File

cProfile generates profile.out after main.py completes. We can use cprofilev to look into the details:

cprofilev -a -p 3214 -f profile.out main.py

where -a specifies the HTTP IP, -p specifies the port, -f specifies the profiling file, and main.py is the source file.

Open the Web browser and points to the local IP and the specifies port, we will see the output like the following:

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.284    0.284   29.514   29.514 main.py:1(<module>)
     4696    0.128    0.000   15.748    0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/executor.py:20(run)
     4696   12.040    0.003   12.040    0.003 {built-in method run}
        1    0.144    0.144    6.534    6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)

where each line corresponds to Python function, and the meaning of each column is as follows:

column meaning
ncalls the number of calls into a function
tottime the total execution time of the function, not including the execution time of other functions called by the function
percall tottime divided by ncalls
cumtime the total execution time of the function, including the execution time of other functions being called
percall cumtime divided by ncalls
filename:lineno(function) where the function is define

Identify Performance Bottlenecks

Usually, tottime and the related percall time is what we want to focus on. We can sort above profiling file by tottime:

     4696   12.040    0.003   12.040    0.003 {built-in method run}
   300005    0.874    0.000    1.681    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
   107991    0.676    0.000    1.519    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__)
     4697    0.626    0.000    2.291    0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp)
        1    0.618    0.618    0.618    0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1(<module>)

We can see that the most time-consuming function is the built-in method run, which is a C++ function in libpaddle.so. We will explain how to profile C++ code in the next section. At this moment, let’s look into the third function sync_with_cpp, which is a Python function. We can click it to understand more about it:

Called By:

   Ordered by: internal time
   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>

Function                                                                                                 was called by...
                                                                                                             ncalls  tottime  cumtime
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp)  <-    4697    0.626    2.291  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp)
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp)  <-    4696    0.019    2.316  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:487(clone)
                                                                                                                  1    0.000    0.001  /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:534(append_backward)


   Ordered by: internal time
   List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>

The lists of the callers of sync_with_cpp might help us understand how to improve the function definition.

Profiling Python and C++ Code

Generate the Profiling File

To profile a mixture of Python and C++ code, we can use a Python package, yep, that can work with Google’s perftools, which is a commonly-used profiler for C/C++ code.

In Ubuntu systems, we can install yep and perftools by running the following commands:

apt update
apt install libgoogle-perftools-dev
pip install yep

Then we can run the following command

python -m yep -v main.py

to generate the profiling file. The default filename is main.py.prof.

Please be aware of the -v command line option, which prints the analysis results after generating the profiling file. By examining the the print result, we’d know that if we stripped debug information from libpaddle.so at build time. The following hints help make sure that the analysis results are readable:

  1. Use GCC command line option -g when building libpaddle.so so to include the debug information. The standard building system of PaddlePaddle is CMake, so you might want to set CMAKE_BUILD_TYPE=RelWithDebInfo.

  2. Use GCC command line option -O2 or -O3 to generate optimized binary code. It doesn’t make sense to profile libpaddle.so without optimization, because it would anyway run slowly.

  3. Profiling the single-threaded binary file before the multi-threading version, because the latter often generates tangled profiling analysis result. You might want to set environment variable OMP_NUM_THREADS=1 to prevents OpenMP from automatically starting multiple threads.

Examining the Profiling File

The tool we used to examine the profiling file generated by perftools is pprof, which provides a Web-based GUI like cprofilev.

We can rely on the standard Go toolchain to retrieve the source code of pprof and build it:

go get github.com/google/pprof

Then we can use it to profile main.py.prof generated in the previous section:

pprof -http= `which python`  ./main.py.prof

Where -http specifies the IP and port of the HTTP service. Directing our Web browser to the service, we would see something like the following:


Identifying the Performance Bottlenecks

Similar to how we work with cprofilev, we’d focus on tottime and cumtime.


We can see that the execution time of multiplication and the computing of the gradient of multiplication takes 2% to 4% of the total running time, and MomentumOp takes about 17%. Obviously, we’d want to optimize MomentumOp.

pprof would mark performance critical parts of the program in red. It’s a good idea to follow the hints.