Polishing the cpu profiling doc (#6116)

doc/howto/optimization/cpu_profiling.md

-This tutorial introduces techniques we used to profile and tune the
+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 `perftools`.
+`cProfile` and `yep`, and Google's `perftools`.
 
-Profiling is the process that reveals the performance bottlenecks,
+Profiling is the process that reveals performance bottlenecks,
 which could be very different from what's in the developers' mind.
-Performance tuning is to fix the bottlenecks. Performance optimization
+Performance tuning is done to fix these bottlenecks. Performance optimization
 repeats the steps of profiling and tuning alternatively.
 
-PaddlePaddle users program AI by calling the Python API, which calls
+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
 
@@ -82,7 +82,7 @@ focus on. We can sort above profiling file by tottime:
 
 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 the right
+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:
 
@@ -135,8 +135,8 @@ 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 taking a
-glance at the print result, we'd know that if we stripped debug
+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:
 
@@ -155,9 +155,9 @@ help make sure that the analysis results are readable:
    variable `OMP_NUM_THREADS=1` to prevents OpenMP from automatically
    starting multiple threads.
 
-### Look into the Profiling File
+### Examining the Profiling File
 
-The tool we used to look into the profiling file generated by
+The tool we used to examine the profiling file generated by
 `perftools` is [`pprof`](https://github.com/google/pprof), which
 provides a Web-based GUI like `cprofilev`.
 
@@ -194,4 +194,4 @@ 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 hint.
+red. It's a good idea to follow the hints.