Profiling (internal)
====================

This document details, for Ray developers, how to analyze Ray performance.

Getting a stack trace of Ray C++ processes
------------------------------------------

You can use the following GDB command to view the current stack trace of any
running Ray process (e.g., raylet). This can be useful for debugging 100% CPU
utilization or infinite loops (simply run the command a few times to see what
the process is stuck on).

.. code-block:: shell

 sudo gdb -batch -ex "thread apply all bt" -p <pid>

Note that you can find the pid of the raylet with ``pgrep raylet``.

Installation
------------

These instructions are for Ubuntu only. Attempts to get ``pprof`` to correctly
symbolize on Mac OS have failed.

.. code-block:: bash

  sudo apt-get install google-perftools libgoogle-perftools-dev

Launching the to-profile binary
-------------------------------

If you want to launch Ray in profiling mode, define the following variables:

.. code-block:: bash

  export RAYLET_PERFTOOLS_PATH=/usr/lib/x86_64-linux-gnu/libprofiler.so
  export RAYLET_PERFTOOLS_LOGFILE=/tmp/pprof.out


The file ``/tmp/pprof.out`` will be empty until you let the binary run the
target workload for a while and then ``kill`` it via ``ray stop`` or by
letting the driver exit.

Visualizing the CPU profile
---------------------------

The output of ``pprof`` can be visualized in many ways. Here we output it as a
zoomable ``.svg`` image displaying the call graph annotated with hot paths.

.. code-block:: bash

  # Use the appropriate path.
  RAYLET=ray/python/ray/core/src/ray/raylet/raylet

  google-pprof -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg
  # Then open the .svg file with Chrome.

  # If you realize the call graph is too large, use -focus=<some function> to zoom
  # into subtrees.
  google-pprof -focus=epoll_wait -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg

Here's a snapshot of an example svg output, taken from the official
documentation:

.. image:: http://goog-perftools.sourceforge.net/doc/pprof-test-big.gif

Running Microbenchmarks
-----------------------

To run a set of single-node Ray microbenchmarks, use:

.. code-block:: bash

  ray microbenchmark

You can find the microbenchmark results for Ray releases in the `GitHub release logs <https://github.com/ray-project/ray/tree/master/release/release_logs>`__.

References
----------

- The `pprof documentation <http://goog-perftools.sourceforge.net/doc/cpu_profiler.html>`_.
- A `Go version of pprof <https://github.com/google/pprof>`_.
- The `gperftools <https://github.com/gperftools/gperftools>`_, including libprofiler, tcmalloc, and other goodies.