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267 lines
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ReStructuredText
267 lines
12 KiB
ReStructuredText
.. highlight:: shell-session
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.. _perf_profiling:
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==============================================
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Python support for the Linux ``perf`` profiler
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==============================================
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:author: Pablo Galindo
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`The Linux perf profiler <https://perf.wiki.kernel.org>`_
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is a very powerful tool that allows you to profile and obtain
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information about the performance of your application.
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``perf`` also has a very vibrant ecosystem of tools
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that aid with the analysis of the data that it produces.
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The main problem with using the ``perf`` profiler with Python applications is that
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``perf`` only gets information about native symbols, that is, the names of
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functions and procedures written in C. This means that the names and file names
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of Python functions in your code will not appear in the output of ``perf``.
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Since Python 3.12, the interpreter can run in a special mode that allows Python
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functions to appear in the output of the ``perf`` profiler. When this mode is
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enabled, the interpreter will interpose a small piece of code compiled on the
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fly before the execution of every Python function and it will teach ``perf`` the
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relationship between this piece of code and the associated Python function using
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:doc:`perf map files <../c-api/perfmaps>`.
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.. note::
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Support for the ``perf`` profiler is currently only available for Linux on
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select architectures. Check the output of the ``configure`` build step or
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check the output of ``python -m sysconfig | grep HAVE_PERF_TRAMPOLINE``
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to see if your system is supported.
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For example, consider the following script:
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.. code-block:: python
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def foo(n):
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result = 0
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for _ in range(n):
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result += 1
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return result
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def bar(n):
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foo(n)
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def baz(n):
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bar(n)
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if __name__ == "__main__":
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baz(1000000)
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We can run ``perf`` to sample CPU stack traces at 9999 hertz::
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$ perf record -F 9999 -g -o perf.data python my_script.py
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Then we can use ``perf report`` to analyze the data:
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.. code-block:: shell-session
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$ perf report --stdio -n -g
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# Children Self Samples Command Shared Object Symbol
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# ........ ........ ............ .......... .................. ..........................................
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#
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91.08% 0.00% 0 python.exe python.exe [.] _start
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---_start
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--90.71%--__libc_start_main
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Py_BytesMain
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|--56.88%--pymain_run_python.constprop.0
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| |
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| |--56.13%--_PyRun_AnyFileObject
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| | _PyRun_SimpleFileObject
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| | |
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| | |--55.02%--run_mod
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| | | --54.65%--PyEval_EvalCode
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | |--51.67%--_PyEval_EvalFrameDefault
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| | | | |--11.52%--_PyLong_Add
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| | | | | |--2.97%--_PyObject_Malloc
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...
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As you can see, the Python functions are not shown in the output, only ``_PyEval_EvalFrameDefault``
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(the function that evaluates the Python bytecode) shows up. Unfortunately that's not very useful because all Python
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functions use the same C function to evaluate bytecode so we cannot know which Python function corresponds to which
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bytecode-evaluating function.
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Instead, if we run the same experiment with ``perf`` support enabled we get:
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.. code-block:: shell-session
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$ perf report --stdio -n -g
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# Children Self Samples Command Shared Object Symbol
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# ........ ........ ............ .......... .................. .....................................................................
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#
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90.58% 0.36% 1 python.exe python.exe [.] _start
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---_start
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--89.86%--__libc_start_main
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Py_BytesMain
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|--55.43%--pymain_run_python.constprop.0
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| |
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| |--54.71%--_PyRun_AnyFileObject
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| | _PyRun_SimpleFileObject
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| | |--53.62%--run_mod
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| | | --53.26%--PyEval_EvalCode
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| | | py::<module>:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::baz:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::bar:/src/script.py
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| | | _PyEval_EvalFrameDefault
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| | | PyObject_Vectorcall
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| | | _PyEval_Vector
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| | | py::foo:/src/script.py
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| | | |--51.81%--_PyEval_EvalFrameDefault
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| | | | |--13.77%--_PyLong_Add
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| | | | | |--3.26%--_PyObject_Malloc
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How to enable ``perf`` profiling support
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----------------------------------------
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``perf`` profiling support can be enabled either from the start using
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the environment variable :envvar:`PYTHONPERFSUPPORT` or the
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:option:`-X perf <-X>` option,
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or dynamically using :func:`sys.activate_stack_trampoline` and
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:func:`sys.deactivate_stack_trampoline`.
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The :mod:`!sys` functions take precedence over the :option:`!-X` option,
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the :option:`!-X` option takes precedence over the environment variable.
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Example, using the environment variable::
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$ PYTHONPERFSUPPORT=1 perf record -F 9999 -g -o perf.data python script.py
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$ perf report -g -i perf.data
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Example, using the :option:`!-X` option::
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$ perf record -F 9999 -g -o perf.data python -X perf script.py
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$ perf report -g -i perf.data
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Example, using the :mod:`sys` APIs in file :file:`example.py`:
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.. code-block:: python
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import sys
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sys.activate_stack_trampoline("perf")
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do_profiled_stuff()
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sys.deactivate_stack_trampoline()
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non_profiled_stuff()
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...then::
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$ perf record -F 9999 -g -o perf.data python ./example.py
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$ perf report -g -i perf.data
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How to obtain the best results
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------------------------------
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For best results, Python should be compiled with
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``CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer"`` as this allows
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profilers to unwind using only the frame pointer and not on DWARF debug
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information. This is because as the code that is interposed to allow ``perf``
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support is dynamically generated it doesn't have any DWARF debugging information
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available.
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You can check if your system has been compiled with this flag by running::
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$ python -m sysconfig | grep 'no-omit-frame-pointer'
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If you don't see any output it means that your interpreter has not been compiled with
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frame pointers and therefore it may not be able to show Python functions in the output
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of ``perf``.
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How to work without frame pointers
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----------------------------------
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If you are working with a Python interpreter that has been compiled without
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frame pointers, you can still use the ``perf`` profiler, but the overhead will be
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a bit higher because Python needs to generate unwinding information for every
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Python function call on the fly. Additionally, ``perf`` will take more time to
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process the data because it will need to use the DWARF debugging information to
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unwind the stack and this is a slow process.
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To enable this mode, you can use the environment variable
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:envvar:`PYTHON_PERF_JIT_SUPPORT` or the :option:`-X perf_jit <-X>` option,
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which will enable the JIT mode for the ``perf`` profiler.
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.. note::
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Due to a bug in the ``perf`` tool, only ``perf`` versions higher than v6.8
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will work with the JIT mode. The fix was also backported to the v6.7.2
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version of the tool.
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Note that when checking the version of the ``perf`` tool (which can be done
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by running ``perf version``) you must take into account that some distros
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add some custom version numbers including a ``-`` character. This means
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that ``perf 6.7-3`` is not necessarily ``perf 6.7.3``.
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When using the perf JIT mode, you need an extra step before you can run ``perf
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report``. You need to call the ``perf inject`` command to inject the JIT
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information into the ``perf.data`` file.::
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$ perf record -F 9999 -g --call-graph dwarf -o perf.data python -Xperf_jit my_script.py
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$ perf inject -i perf.data --jit --output perf.jit.data
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$ perf report -g -i perf.jit.data
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or using the environment variable::
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$ PYTHON_PERF_JIT_SUPPORT=1 perf record -F 9999 -g --call-graph dwarf -o perf.data python my_script.py
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$ perf inject -i perf.data --jit --output perf.jit.data
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$ perf report -g -i perf.jit.data
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``perf inject --jit`` command will read ``perf.data``,
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automatically pick up the perf dump file that Python creates (in
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``/tmp/perf-$PID.dump``), and then create ``perf.jit.data`` which merges all the
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JIT information together. It should also create a lot of ``jitted-XXXX-N.so``
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files in the current directory which are ELF images for all the JIT trampolines
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that were created by Python.
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.. warning::
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Notice that when using ``--call-graph dwarf`` the ``perf`` tool will take
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snapshots of the stack of the process being profiled and save the
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information in the ``perf.data`` file. By default the size of the stack dump
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is 8192 bytes but the user can change the size by passing the size after
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comma like ``--call-graph dwarf,4096``. The size of the stack dump is
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important because if the size is too small ``perf`` will not be able to
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unwind the stack and the output will be incomplete. On the other hand, if
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the size is too big, then ``perf`` won't be able to sample the process as
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frequently as it would like as the overhead will be higher.
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