docs/develop: Import oco's article on profiling applications

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  well

docs/develop/debugging/optimizing_applications.rst

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+Profiling and optimizing applications
+#####################################
+
+This article present how to use tools available under Haiku to analyze performance of Haiku applications in order to Optimize them.
+
+Tools used in this tutorial :
+
+- profile
+- c++filt
+- QCachegrind
+
+The first two comes with Haiku.
+The latest need to be installed using HaikuDepot (GUI) or pkgman (command line).
+
+Overview of the process
+=======================
+
+This is basically a three steps process:
+
+- identify a scenario that appears to be slow
+- measure the performance using profile
+- analyze results using QCachegrind
+
+Measure
+-------
+
+profile is a `sample-based profiler<https://nikhilism.com/post/2018/sampling-profiler-internals-introduction/>`_: this tool stops the measured process at regular intervals, and looks at the call stack at this point. The more you stop in a specified function, the more likely this function eats CPU.
+
+All data is consolidated and written in the output directory.
+
+::
+
+    profile -v [output directory] -i [tick interval in ms] [process to start]
+
+The -v option generates data in a format suitable for QCachegrind that we will use later.
+
+Example:
+
+::
+
+    profile -v output_dir -i 300 HaikuDepot
+
+This example generates one file per thread. Depending on what you are looking for, you might prefer to have everything in one file. You can do this using the -S option:
+
+::
+
+    profile -v output_dir -i 300 -S HaikuDepot
+
+Then, you should replay your test case in the launched application. When you close it, measures are written in output directory specified with the -v option.
+
+More options in profile are available. See profile --help
+
+Tips
+----
+
+Haiku is mainly written in C++. Function names in C++ binaries are encoded using a specific scheme. This process is called `mangling<https://en.wikipedia.org/wiki/Name_mangling>`_.
+In order to have more readable functions name in the analysis, you can use C++filt to pre-process result’s files.
+
+::
+
+    c++filt < [measureFile] > [resultFile]
+
+example :
+
+::
+
+    c++filt < measure_file > unmangled_measure_file
+
+Analyze
+-------
+
+Finally, you should start QCachegrind. From there, open the unmangled_measure_file that you have generated at the previous step.
+
+.. image:: qcachegrind_analyze.png
+
+Here, the SearchTermsFilter::AcceptsPackage is hit in 46 % of the samples (first column on the left). If you look on the right view, the callee map shows all functions called from AcceptsPackage with a proportional area. Graphically, we see that ToLower is hit almost 65 % of the time.
+
+The result of this analysis is that you should look at this part of the code to identify a way to optimize this path.
+
+.. image:: qcachegrind_analyze_findrect.png
+
+This view shows another frequent case: the OutlineView::FindRect function has a large Self %: almost 65 % (large green rectangle). This pinpoints the fact that most of the time, we stop in the function itself, not in a subfunction. Maybe, there is something to investigate in the implementation of this function. Or maybe this function is called too often.