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Initial commit of OpenSPARC T2 design and verification files.
[OpenSPARC-T2-DV] / tools / src / nas,5.n2.os.2 / lib / python / html / python / lib / profile-instant.html
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55<H1><A NAME="SECTION0012300000000000000000"></A><A NAME="profile-instant"></A>
56<BR>
5710.3 Instant Users Manual
58</H1>
59
60<P>
61This section is provided for users that ``don't want to read the
62manual.'' It provides a very brief overview, and allows a user to
63rapidly perform profiling on an existing application.
64
65<P>
66To profile an application with a main entry point of <tt class="function">foo()</tt>,
67you would add the following to your module:
68
69<P>
70<div class="verbatim"><pre>
71import profile
72profile.run('foo()')
73</pre></div>
74
75<P>
76The above action would cause <tt class="function">foo()</tt> to be run, and a series of
77informative lines (the profile) to be printed. The above approach is
78most useful when working with the interpreter. If you would like to
79save the results of a profile into a file for later examination, you
80can supply a file name as the second argument to the <tt class="function">run()</tt>
81function:
82
83<P>
84<div class="verbatim"><pre>
85import profile
86profile.run('foo()', 'fooprof')
87</pre></div>
88
89<P>
90The file <span class="file">profile.py</span> can also be invoked as
91a script to profile another script. For example:
92
93<P>
94<div class="verbatim"><pre>
95python -m profile myscript.py
96</pre></div>
97
98<P>
99<span class="file">profile.py</span> accepts two optional arguments on the command line:
100
101<P>
102<div class="verbatim"><pre>
103profile.py [-o output_file] [-s sort_order]
104</pre></div>
105
106<P>
107<b class="programopt">-s</b> only applies to standard output (<b class="programopt">-o</b> is
108not supplied). Look in the <tt class="class">Stats</tt> documentation for valid sort
109values.
110
111<P>
112When you wish to review the profile, you should use the methods in the
113<tt class="module">pstats</tt> module. Typically you would load the statistics data as
114follows:
115
116<P>
117<div class="verbatim"><pre>
118import pstats
119p = pstats.Stats('fooprof')
120</pre></div>
121
122<P>
123The class <tt class="class">Stats</tt> (the above code just created an instance of
124this class) has a variety of methods for manipulating and printing the
125data that was just read into <code>p</code>. When you ran
126<tt class="function">profile.run()</tt> above, what was printed was the result of three
127method calls:
128
129<P>
130<div class="verbatim"><pre>
131p.strip_dirs().sort_stats(-1).print_stats()
132</pre></div>
133
134<P>
135The first method removed the extraneous path from all the module
136names. The second method sorted all the entries according to the
137standard module/line/name string that is printed (this is to comply
138with the semantics of the old profiler). The third method printed out
139all the statistics. You might try the following sort calls:
140
141<P>
142<div class="verbatim"><pre>
143p.sort_stats('name')
144p.print_stats()
145</pre></div>
146
147<P>
148The first call will actually sort the list by function name, and the
149second call will print out the statistics. The following are some
150interesting calls to experiment with:
151
152<P>
153<div class="verbatim"><pre>
154p.sort_stats('cumulative').print_stats(10)
155</pre></div>
156
157<P>
158This sorts the profile by cumulative time in a function, and then only
159prints the ten most significant lines. If you want to understand what
160algorithms are taking time, the above line is what you would use.
161
162<P>
163If you were looking to see what functions were looping a lot, and
164taking a lot of time, you would do:
165
166<P>
167<div class="verbatim"><pre>
168p.sort_stats('time').print_stats(10)
169</pre></div>
170
171<P>
172to sort according to time spent within each function, and then print
173the statistics for the top ten functions.
174
175<P>
176You might also try:
177
178<P>
179<div class="verbatim"><pre>
180p.sort_stats('file').print_stats('__init__')
181</pre></div>
182
183<P>
184This will sort all the statistics by file name, and then print out
185statistics for only the class init methods (since they are spelled
186with <code>__init__</code> in them). As one final example, you could try:
187
188<P>
189<div class="verbatim"><pre>
190p.sort_stats('time', 'cum').print_stats(.5, 'init')
191</pre></div>
192
193<P>
194This line sorts statistics with a primary key of time, and a secondary
195key of cumulative time, and then prints out some of the statistics.
196To be specific, the list is first culled down to 50% (re: "<tt class="samp">.5</tt>")
197of its original size, then only lines containing <code>init</code> are
198maintained, and that sub-sub-list is printed.
199
200<P>
201If you wondered what functions called the above functions, you could
202now (<code>p</code> is still sorted according to the last criteria) do:
203
204<P>
205<div class="verbatim"><pre>
206p.print_callers(.5, 'init')
207</pre></div>
208
209<P>
210and you would get a list of callers for each of the listed functions.
211
212<P>
213If you want more functionality, you're going to have to read the
214manual, or guess what the following functions do:
215
216<P>
217<div class="verbatim"><pre>
218p.print_callees()
219p.add('fooprof')
220</pre></div>
221
222<P>
223Invoked as a script, the <tt class="module">pstats</tt> module is a statistics
224browser for reading and examining profile dumps. It has a simple
225line-oriented interface (implemented using <tt class="module"><a href="module-cmd.html">cmd</a></tt>) and
226interactive help.
227
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264<span class="release-info">Release 2.4.2, documentation updated on 28 September 2005.</span>
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Full OpenSPARC design & verification tarball including full HDL.