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Add documentation for sample profiling support.
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Summary:
This documents the usage of sample profilers with Clang and the
profile format expected by LLVM's optimizers. It also documents the
profile conversion tool used by Linux Perf.

Reviewers: doug.gregor

CC: cfe-commits

Differential Revision: http://reviews.llvm.org/D3402

llvm-svn: 206994
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dnovillo committed Apr 23, 2014
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Expand Up @@ -1065,6 +1065,135 @@ are listed below.
only. This only applies to the AArch64 architecture.


Using Sampling Profilers for Optimization
-----------------------------------------

Sampling profilers are used to collect runtime information, such as
hardware counters, while your application executes. They are typically
very efficient and do not incur in a large runtime overhead. The
sample data collected by the profiler can be used during compilation
to determine what are the most executed areas of the code.

In particular, sample profilers can provide execution counts for all
instructions in the code, information on branches taken and function
invocation. The compiler can use this information in its optimization
cost models. For example, knowing that a branch is taken very
frequently helps the compiler make better decisions when ordering
basic blocks. Knowing that a function ``foo`` is called more
frequently than another ``bar`` helps the inliner.

Using the data from a sample profiler requires some changes in the way
a program is built. Before the compiler can use profiling information,
the code needs to execute under the profiler. The following is the
usual build cycle when using sample profilers for optimization:

1. Build the code with source line table information. You can use all the
usual build flags that you always build your application with. The only
requirement is that you add ``-gline-tables-ony`` or ``-g`` to the
command line. This is important for the profiler to be able to map
instructions back to source line locations.

.. code-block:: console
$ clang++ -O2 -gline-tables-only code.cc -o code
2. Run the executable under a sampling profiler. The specific profiler
you use does not really matter, as long as its output can be converted
into the format that the LLVM optimizer understands. Currently, there
exists a conversion tool for the Linux Perf profiler
(https://perf.wiki.kernel.org/), so these examples assume that you
are using Linux Perf to profile your code.

.. code-block:: console
$ perf record -b ./code
Note the use of the ``-b`` flag. This tells Perf to use the Last Branch
Record (LBR) to record call chains. While this is not strictly required,
it provides better call information, which improves the accuracy of
the profile data.

3. Convert the collected profile data to LLVM's sample profile format.
This is currently supported via the AutoFDO converter ``create_llvm_prof``.
It is available at http://github.com/google/autofdo. Once built and
installed, you can convert the ``perf.data`` file to LLVM using
the command:

.. code-block:: console
$ create_llvm_prof --binary=./code --out=code.prof
This will read ``perf.data``, the binary file ``./code`` and emit
the profile data in ``code.prof``. Note that if you ran ``perf``
without the ``-b`` flag, you need to use ``--use_lbr=false`` when
calling ``create_llvm_prof``.

4. Build the code again using the collected profile. This step feeds
the profile back to the optimizers. This should result in a binary
that executes faster than the original one.

.. code-block:: console
$ clang++ -O2 -gline-tables-only -fprofile-sample-use=code.prof code.cc -o code
Sample Profile Format
^^^^^^^^^^^^^^^^^^^^^

If you are not using Linux Perf to collect profiles, you will need to
write a conversion tool from your profiler to LLVM's format. This section
explains the file format expected by the backend.

Sample profiles are written as ASCII text. The file is divided into sections,
which correspond to each of the functions executed at runtime. Each
section has the following format (taken from
https://github.com/google/autofdo/blob/master/profile_writer.h):

.. code-block:: console
function1:total_samples:total_head_samples
offset1[.discriminator]: number_of_samples [fn1:num fn2:num ... ]
offset2[.discriminator]: number_of_samples [fn3:num fn4:num ... ]
...
offsetN[.discriminator]: number_of_samples [fn5:num fn6:num ... ]
Function names must be mangled in order for the profile loader to
match them in the current translation unit. The two numbers in the
function header specify how many total samples were accumulated in the
function (first number), and the total number of samples accumulated
at the prologue of the function (second number). This head sample
count provides an indicator of how frequent is the function invoked.

Each sampled line may contain several items. Some are optional (marked
below):

a. Source line offset. This number represents the line number
in the function where the sample was collected. The line number is
always relative to the line where symbol of the function is
defined. So, if the function has its header at line 280, the offset
13 is at line 293 in the file.

b. [OPTIONAL] Discriminator. This is used if the sampled program
was compiled with DWARF discriminator support
(http://wiki.dwarfstd.org/index.php?title=Path_Discriminators)

c. Number of samples. This is the number of samples collected by
the profiler at this source location.

d. [OPTIONAL] Potential call targets and samples. If present, this
line contains a call instruction. This models both direct and
indirect calls. Each called target is listed together with the
number of samples. For example,

.. code-block:: console
130: 7 foo:3 bar:2 baz:7
The above means that at relative line offset 130 there is a call
instruction that calls one of ``foo()``, ``bar()`` and ``baz()``.
With ``baz()`` being the relatively more frequent call target.


Controlling Size of Debug Information
-------------------------------------

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