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Link to Using C++ profilers page in CPU optimization
This page was created after CPU optimization, so it wasn't linked beforehand.
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tutorials/performance/cpu_optimization.rst

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@@ -56,19 +56,8 @@ External profilers
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Although the Godot IDE profiler is very convenient and useful, sometimes you
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need more power, and the ability to profile the Godot engine source code itself.
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You can use a number of third party profilers to do this including
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`Valgrind <https://www.valgrind.org/>`__,
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`VerySleepy <http://www.codersnotes.com/sleepy/>`__,
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`HotSpot <https://github.com/KDAB/hotspot>`__,
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`Visual Studio <https://visualstudio.microsoft.com/>`__ and
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`Intel VTune <https://software.intel.com/content/www/us/en/develop/tools/vtune-profiler.html>`__.
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.. note:: You will need to compile Godot from source to use a third-party profiler.
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This is required to obtain debugging symbols. You can also use a debug
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build, however, note that the results of profiling a debug build will
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be different to a release build, because debug builds are less
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optimized. Bottlenecks are often in a different place in debug builds,
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so you should profile release builds whenever possible.
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You can :ref:`use a number of third-party C++ profilers <doc_using_cpp_profilers>`
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to do this.
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.. figure:: img/valgrind.png
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:alt: Screenshot of Callgrind
@@ -81,7 +70,7 @@ itself, excluding child functions (Self), the number of times the function is
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called, the function name, and the file or module.
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In this example, we can see nearly all time is spent under the
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`Main::iteration()` function. This is the master function in the Godot source
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``Main::iteration()`` function. This is the master function in the Godot source
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code that is called repeatedly. It causes frames to be drawn, physics ticks to
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be simulated, and nodes and scripts to be updated. A large proportion of the
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time is spent in the functions to render a canvas (66%), because this example
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=========
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Although Nodes are an incredibly powerful and versatile concept, be aware that
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every node has a cost. Built-in functions such as `_process()` and
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`_physics_process()` propagate through the tree. This housekeeping can reduce
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every node has a cost. Built-in functions such as ``_process()`` and
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``_physics_process()`` propagate through the tree. This housekeeping can reduce
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performance when you have a very large numbers of nodes (how many exactly
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depends on the target platform and can range from thousands to tens of
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thousands so ensure that you profile performance on all target platforms

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