| \chapter{The Python Profiler \label{profile}} |
| |
| \sectionauthor{James Roskind}{} |
| |
| Copyright \copyright{} 1994, by InfoSeek Corporation, all rights reserved. |
| \index{InfoSeek Corporation} |
| |
| Written by James Roskind.\footnote{ |
| Updated and converted to \LaTeX\ by Guido van Rossum. The references to |
| the old profiler are left in the text, although it no longer exists.} |
| |
| Permission to use, copy, modify, and distribute this Python software |
| and its associated documentation for any purpose (subject to the |
| restriction in the following sentence) without fee is hereby granted, |
| provided that the above copyright notice appears in all copies, and |
| that both that copyright notice and this permission notice appear in |
| supporting documentation, and that the name of InfoSeek not be used in |
| advertising or publicity pertaining to distribution of the software |
| without specific, written prior permission. This permission is |
| explicitly restricted to the copying and modification of the software |
| to remain in Python, compiled Python, or other languages (such as C) |
| wherein the modified or derived code is exclusively imported into a |
| Python module. |
| |
| INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS |
| SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND |
| FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY |
| SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER |
| RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF |
| CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN |
| CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. |
| |
| |
| The profiler was written after only programming in Python for 3 weeks. |
| As a result, it is probably clumsy code, but I don't know for sure yet |
| 'cause I'm a beginner :-). I did work hard to make the code run fast, |
| so that profiling would be a reasonable thing to do. I tried not to |
| repeat code fragments, but I'm sure I did some stuff in really awkward |
| ways at times. Please send suggestions for improvements to: |
| \email{jar@netscape.com}. I won't promise \emph{any} support. ...but |
| I'd appreciate the feedback. |
| |
| |
| \section{Introduction to the profiler} |
| \nodename{Profiler Introduction} |
| |
| A \dfn{profiler} is a program that describes the run time performance |
| of a program, providing a variety of statistics. This documentation |
| describes the profiler functionality provided in the modules |
| \module{profile} and \module{pstats}. This profiler provides |
| \dfn{deterministic profiling} of any Python programs. It also |
| provides a series of report generation tools to allow users to rapidly |
| examine the results of a profile operation. |
| \index{deterministic profiling} |
| \index{profiling, deterministic} |
| |
| |
| \section{How Is This Profiler Different From The Old Profiler?} |
| \nodename{Profiler Changes} |
| |
| (This section is of historical importance only; the old profiler |
| discussed here was last seen in Python 1.1.) |
| |
| The big changes from old profiling module are that you get more |
| information, and you pay less CPU time. It's not a trade-off, it's a |
| trade-up. |
| |
| To be specific: |
| |
| \begin{description} |
| |
| \item[Bugs removed:] |
| Local stack frame is no longer molested, execution time is now charged |
| to correct functions. |
| |
| \item[Accuracy increased:] |
| Profiler execution time is no longer charged to user's code, |
| calibration for platform is supported, file reads are not done \emph{by} |
| profiler \emph{during} profiling (and charged to user's code!). |
| |
| \item[Speed increased:] |
| Overhead CPU cost was reduced by more than a factor of two (perhaps a |
| factor of five), lightweight profiler module is all that must be |
| loaded, and the report generating module (\module{pstats}) is not needed |
| during profiling. |
| |
| \item[Recursive functions support:] |
| Cumulative times in recursive functions are correctly calculated; |
| recursive entries are counted. |
| |
| \item[Large growth in report generating UI:] |
| Distinct profiles runs can be added together forming a comprehensive |
| report; functions that import statistics take arbitrary lists of |
| files; sorting criteria is now based on keywords (instead of 4 integer |
| options); reports shows what functions were profiled as well as what |
| profile file was referenced; output format has been improved. |
| |
| \end{description} |
| |
| |
| \section{Instant Users Manual \label{profile-instant}} |
| |
| This section is provided for users that ``don't want to read the |
| manual.'' It provides a very brief overview, and allows a user to |
| rapidly perform profiling on an existing application. |
| |
| To profile an application with a main entry point of \samp{foo()}, you |
| would add the following to your module: |
| |
| \begin{verbatim} |
| import profile |
| profile.run('foo()') |
| \end{verbatim} |
| |
| The above action would cause \samp{foo()} to be run, and a series of |
| informative lines (the profile) to be printed. The above approach is |
| most useful when working with the interpreter. If you would like to |
| save the results of a profile into a file for later examination, you |
| can supply a file name as the second argument to the \function{run()} |
| function: |
| |
| \begin{verbatim} |
| import profile |
| profile.run('foo()', 'fooprof') |
| \end{verbatim} |
| |
| The file \file{profile.py} can also be invoked as |
| a script to profile another script. For example: |
| |
| \begin{verbatim} |
| python /usr/local/lib/python1.5/profile.py myscript.py |
| \end{verbatim} |
| |
| When you wish to review the profile, you should use the methods in the |
| \module{pstats} module. Typically you would load the statistics data as |
| follows: |
| |
| \begin{verbatim} |
| import pstats |
| p = pstats.Stats('fooprof') |
| \end{verbatim} |
| |
| The class \class{Stats} (the above code just created an instance of |
| this class) has a variety of methods for manipulating and printing the |
| data that was just read into \samp{p}. When you ran |
| \function{profile.run()} above, what was printed was the result of three |
| method calls: |
| |
| \begin{verbatim} |
| p.strip_dirs().sort_stats(-1).print_stats() |
| \end{verbatim} |
| |
| The first method removed the extraneous path from all the module |
| names. The second method sorted all the entries according to the |
| standard module/line/name string that is printed (this is to comply |
| with the semantics of the old profiler). The third method printed out |
| all the statistics. You might try the following sort calls: |
| |
| \begin{verbatim} |
| p.sort_stats('name') |
| p.print_stats() |
| \end{verbatim} |
| |
| The first call will actually sort the list by function name, and the |
| second call will print out the statistics. The following are some |
| interesting calls to experiment with: |
| |
| \begin{verbatim} |
| p.sort_stats('cumulative').print_stats(10) |
| \end{verbatim} |
| |
| This sorts the profile by cumulative time in a function, and then only |
| prints the ten most significant lines. If you want to understand what |
| algorithms are taking time, the above line is what you would use. |
| |
| If you were looking to see what functions were looping a lot, and |
| taking a lot of time, you would do: |
| |
| \begin{verbatim} |
| p.sort_stats('time').print_stats(10) |
| \end{verbatim} |
| |
| to sort according to time spent within each function, and then print |
| the statistics for the top ten functions. |
| |
| You might also try: |
| |
| \begin{verbatim} |
| p.sort_stats('file').print_stats('__init__') |
| \end{verbatim} |
| |
| This will sort all the statistics by file name, and then print out |
| statistics for only the class init methods ('cause they are spelled |
| with \samp{__init__} in them). As one final example, you could try: |
| |
| \begin{verbatim} |
| p.sort_stats('time', 'cum').print_stats(.5, 'init') |
| \end{verbatim} |
| |
| This line sorts statistics with a primary key of time, and a secondary |
| key of cumulative time, and then prints out some of the statistics. |
| To be specific, the list is first culled down to 50\% (re: \samp{.5}) |
| of its original size, then only lines containing \code{init} are |
| maintained, and that sub-sub-list is printed. |
| |
| If you wondered what functions called the above functions, you could |
| now (\samp{p} is still sorted according to the last criteria) do: |
| |
| \begin{verbatim} |
| p.print_callers(.5, 'init') |
| \end{verbatim} |
| |
| and you would get a list of callers for each of the listed functions. |
| |
| If you want more functionality, you're going to have to read the |
| manual, or guess what the following functions do: |
| |
| \begin{verbatim} |
| p.print_callees() |
| p.add('fooprof') |
| \end{verbatim} |
| |
| Invoked as a script, the \module{pstats} module is a statistics |
| browser for reading and examining profile dumps. It has a simple |
| line-oriented interface (implemented using \refmodule{cmd}) and |
| interactive help. |
| |
| \section{What Is Deterministic Profiling?} |
| \nodename{Deterministic Profiling} |
| |
| \dfn{Deterministic profiling} is meant to reflect the fact that all |
| \emph{function call}, \emph{function return}, and \emph{exception} events |
| are monitored, and precise timings are made for the intervals between |
| these events (during which time the user's code is executing). In |
| contrast, \dfn{statistical profiling} (which is not done by this |
| module) randomly samples the effective instruction pointer, and |
| deduces where time is being spent. The latter technique traditionally |
| involves less overhead (as the code does not need to be instrumented), |
| but provides only relative indications of where time is being spent. |
| |
| In Python, since there is an interpreter active during execution, the |
| presence of instrumented code is not required to do deterministic |
| profiling. Python automatically provides a \dfn{hook} (optional |
| callback) for each event. In addition, the interpreted nature of |
| Python tends to add so much overhead to execution, that deterministic |
| profiling tends to only add small processing overhead in typical |
| applications. The result is that deterministic profiling is not that |
| expensive, yet provides extensive run time statistics about the |
| execution of a Python program. |
| |
| Call count statistics can be used to identify bugs in code (surprising |
| counts), and to identify possible inline-expansion points (high call |
| counts). Internal time statistics can be used to identify ``hot |
| loops'' that should be carefully optimized. Cumulative time |
| statistics should be used to identify high level errors in the |
| selection of algorithms. Note that the unusual handling of cumulative |
| times in this profiler allows statistics for recursive implementations |
| of algorithms to be directly compared to iterative implementations. |
| |
| |
| \section{Reference Manual} |
| |
| \declaremodule{standard}{profile} |
| \modulesynopsis{Python profiler} |
| |
| |
| |
| The primary entry point for the profiler is the global function |
| \function{profile.run()}. It is typically used to create any profile |
| information. The reports are formatted and printed using methods of |
| the class \class{pstats.Stats}. The following is a description of all |
| of these standard entry points and functions. For a more in-depth |
| view of some of the code, consider reading the later section on |
| Profiler Extensions, which includes discussion of how to derive |
| ``better'' profilers from the classes presented, or reading the source |
| code for these modules. |
| |
| \begin{funcdesc}{run}{string\optional{, filename\optional{, ...}}} |
| |
| This function takes a single argument that has can be passed to the |
| \keyword{exec} statement, and an optional file name. In all cases this |
| routine attempts to \keyword{exec} its first argument, and gather profiling |
| statistics from the execution. If no file name is present, then this |
| function automatically prints a simple profiling report, sorted by the |
| standard name string (file/line/function-name) that is presented in |
| each line. The following is a typical output from such a call: |
| |
| \begin{verbatim} |
| main() |
| 2706 function calls (2004 primitive calls) in 4.504 CPU seconds |
| |
| Ordered by: standard name |
| |
| ncalls tottime percall cumtime percall filename:lineno(function) |
| 2 0.006 0.003 0.953 0.477 pobject.py:75(save_objects) |
| 43/3 0.533 0.012 0.749 0.250 pobject.py:99(evaluate) |
| ... |
| \end{verbatim} |
| |
| The first line indicates that this profile was generated by the call:\\ |
| \code{profile.run('main()')}, and hence the exec'ed string is |
| \code{'main()'}. The second line indicates that 2706 calls were |
| monitored. Of those calls, 2004 were \dfn{primitive}. We define |
| \dfn{primitive} to mean that the call was not induced via recursion. |
| The next line: \code{Ordered by:\ standard name}, indicates that |
| the text string in the far right column was used to sort the output. |
| The column headings include: |
| |
| \begin{description} |
| |
| \item[ncalls ] |
| for the number of calls, |
| |
| \item[tottime ] |
| for the total time spent in the given function (and excluding time |
| made in calls to sub-functions), |
| |
| \item[percall ] |
| is the quotient of \code{tottime} divided by \code{ncalls} |
| |
| \item[cumtime ] |
| is the total time spent in this and all subfunctions (from invocation |
| till exit). This figure is accurate \emph{even} for recursive |
| functions. |
| |
| \item[percall ] |
| is the quotient of \code{cumtime} divided by primitive calls |
| |
| \item[filename:lineno(function) ] |
| provides the respective data of each function |
| |
| \end{description} |
| |
| When there are two numbers in the first column (for example, |
| \samp{43/3}), then the latter is the number of primitive calls, and |
| the former is the actual number of calls. Note that when the function |
| does not recurse, these two values are the same, and only the single |
| figure is printed. |
| |
| \end{funcdesc} |
| |
| Analysis of the profiler data is done using this class from the |
| \module{pstats} module: |
| |
| % now switch modules.... |
| % (This \stmodindex use may be hard to change ;-( ) |
| \stmodindex{pstats} |
| |
| \begin{classdesc}{Stats}{filename\optional{, ...}} |
| This class constructor creates an instance of a ``statistics object'' |
| from a \var{filename} (or set of filenames). \class{Stats} objects are |
| manipulated by methods, in order to print useful reports. |
| |
| The file selected by the above constructor must have been created by |
| the corresponding version of \module{profile}. To be specific, there is |
| \emph{no} file compatibility guaranteed with future versions of this |
| profiler, and there is no compatibility with files produced by other |
| profilers (such as the old system profiler). |
| |
| If several files are provided, all the statistics for identical |
| functions will be coalesced, so that an overall view of several |
| processes can be considered in a single report. If additional files |
| need to be combined with data in an existing \class{Stats} object, the |
| \method{add()} method can be used. |
| \end{classdesc} |
| |
| |
| \subsection{The \class{Stats} Class \label{profile-stats}} |
| |
| \class{Stats} objects have the following methods: |
| |
| \begin{methoddesc}[Stats]{strip_dirs}{} |
| This method for the \class{Stats} class removes all leading path |
| information from file names. It is very useful in reducing the size |
| of the printout to fit within (close to) 80 columns. This method |
| modifies the object, and the stripped information is lost. After |
| performing a strip operation, the object is considered to have its |
| entries in a ``random'' order, as it was just after object |
| initialization and loading. If \method{strip_dirs()} causes two |
| function names to be indistinguishable (they are on the same |
| line of the same filename, and have the same function name), then the |
| statistics for these two entries are accumulated into a single entry. |
| \end{methoddesc} |
| |
| |
| \begin{methoddesc}[Stats]{add}{filename\optional{, ...}} |
| This method of the \class{Stats} class accumulates additional |
| profiling information into the current profiling object. Its |
| arguments should refer to filenames created by the corresponding |
| version of \function{profile.run()}. Statistics for identically named |
| (re: file, line, name) functions are automatically accumulated into |
| single function statistics. |
| \end{methoddesc} |
| |
| \begin{methoddesc}[Stats]{dump_stats}{filename} |
| Save the data loaded into the \class{Stats} object to a file named |
| \var{filename}. The file is created if it does not exist, and is |
| overwritten if it already exists. This is equivalent to the method of |
| the same name on the \class{profile.Profile} class. |
| \versionadded{2.3} |
| \end{methoddesc} |
| |
| \begin{methoddesc}[Stats]{sort_stats}{key\optional{, ...}} |
| This method modifies the \class{Stats} object by sorting it according |
| to the supplied criteria. The argument is typically a string |
| identifying the basis of a sort (example: \code{'time'} or |
| \code{'name'}). |
| |
| When more than one key is provided, then additional keys are used as |
| secondary criteria when there is equality in all keys selected |
| before them. For example, \samp{sort_stats('name', 'file')} will sort |
| all the entries according to their function name, and resolve all ties |
| (identical function names) by sorting by file name. |
| |
| Abbreviations can be used for any key names, as long as the |
| abbreviation is unambiguous. The following are the keys currently |
| defined: |
| |
| \begin{tableii}{l|l}{code}{Valid Arg}{Meaning} |
| \lineii{'calls'}{call count} |
| \lineii{'cumulative'}{cumulative time} |
| \lineii{'file'}{file name} |
| \lineii{'module'}{file name} |
| \lineii{'pcalls'}{primitive call count} |
| \lineii{'line'}{line number} |
| \lineii{'name'}{function name} |
| \lineii{'nfl'}{name/file/line} |
| \lineii{'stdname'}{standard name} |
| \lineii{'time'}{internal time} |
| \end{tableii} |
| |
| Note that all sorts on statistics are in descending order (placing |
| most time consuming items first), where as name, file, and line number |
| searches are in ascending order (alphabetical). The subtle |
| distinction between \code{'nfl'} and \code{'stdname'} is that the |
| standard name is a sort of the name as printed, which means that the |
| embedded line numbers get compared in an odd way. For example, lines |
| 3, 20, and 40 would (if the file names were the same) appear in the |
| string order 20, 3 and 40. In contrast, \code{'nfl'} does a numeric |
| compare of the line numbers. In fact, \code{sort_stats('nfl')} is the |
| same as \code{sort_stats('name', 'file', 'line')}. |
| |
| For compatibility with the old profiler, the numeric arguments |
| \code{-1}, \code{0}, \code{1}, and \code{2} are permitted. They are |
| interpreted as \code{'stdname'}, \code{'calls'}, \code{'time'}, and |
| \code{'cumulative'} respectively. If this old style format (numeric) |
| is used, only one sort key (the numeric key) will be used, and |
| additional arguments will be silently ignored. |
| \end{methoddesc} |
| |
| |
| \begin{methoddesc}[Stats]{reverse_order}{} |
| This method for the \class{Stats} class reverses the ordering of the basic |
| list within the object. This method is provided primarily for |
| compatibility with the old profiler. Its utility is questionable |
| now that ascending vs descending order is properly selected based on |
| the sort key of choice. |
| \end{methoddesc} |
| |
| \begin{methoddesc}[Stats]{print_stats}{\optional{restriction, \moreargs}} |
| This method for the \class{Stats} class prints out a report as described |
| in the \function{profile.run()} definition. |
| |
| The order of the printing is based on the last \method{sort_stats()} |
| operation done on the object (subject to caveats in \method{add()} and |
| \method{strip_dirs()}). |
| |
| The arguments provided (if any) can be used to limit the list down to |
| the significant entries. Initially, the list is taken to be the |
| complete set of profiled functions. Each restriction is either an |
| integer (to select a count of lines), or a decimal fraction between |
| 0.0 and 1.0 inclusive (to select a percentage of lines), or a regular |
| expression (to pattern match the standard name that is printed; as of |
| Python 1.5b1, this uses the Perl-style regular expression syntax |
| defined by the \refmodule{re} module). If several restrictions are |
| provided, then they are applied sequentially. For example: |
| |
| \begin{verbatim} |
| print_stats(.1, 'foo:') |
| \end{verbatim} |
| |
| would first limit the printing to first 10\% of list, and then only |
| print functions that were part of filename \samp{.*foo:}. In |
| contrast, the command: |
| |
| \begin{verbatim} |
| print_stats('foo:', .1) |
| \end{verbatim} |
| |
| would limit the list to all functions having file names \samp{.*foo:}, |
| and then proceed to only print the first 10\% of them. |
| \end{methoddesc} |
| |
| |
| \begin{methoddesc}[Stats]{print_callers}{\optional{restriction, \moreargs}} |
| This method for the \class{Stats} class prints a list of all functions |
| that called each function in the profiled database. The ordering is |
| identical to that provided by \method{print_stats()}, and the definition |
| of the restricting argument is also identical. For convenience, a |
| number is shown in parentheses after each caller to show how many |
| times this specific call was made. A second non-parenthesized number |
| is the cumulative time spent in the function at the right. |
| \end{methoddesc} |
| |
| \begin{methoddesc}[Stats]{print_callees}{\optional{restriction, \moreargs}} |
| This method for the \class{Stats} class prints a list of all function |
| that were called by the indicated function. Aside from this reversal |
| of direction of calls (re: called vs was called by), the arguments and |
| ordering are identical to the \method{print_callers()} method. |
| \end{methoddesc} |
| |
| \begin{methoddesc}[Stats]{ignore}{} |
| \deprecated{1.5.1}{This is not needed in modern versions of |
| Python.\footnote{ |
| This was once necessary, when Python would print any unused expression |
| result that was not \code{None}. The method is still defined for |
| backward compatibility.}} |
| \end{methoddesc} |
| |
| |
| \section{Limitations \label{profile-limits}} |
| |
| There are two fundamental limitations on this profiler. The first is |
| that it relies on the Python interpreter to dispatch \dfn{call}, |
| \dfn{return}, and \dfn{exception} events. Compiled \C{} code does not |
| get interpreted, and hence is ``invisible'' to the profiler. All time |
| spent in \C{} code (including built-in functions) will be charged to the |
| Python function that invoked the \C{} code. If the \C{} code calls out |
| to some native Python code, then those calls will be profiled |
| properly. |
| |
| The second limitation has to do with accuracy of timing information. |
| There is a fundamental problem with deterministic profilers involving |
| accuracy. The most obvious restriction is that the underlying ``clock'' |
| is only ticking at a rate (typically) of about .001 seconds. Hence no |
| measurements will be more accurate than the underlying clock. If |
| enough measurements are taken, then the ``error'' will tend to average |
| out. Unfortunately, removing this first error induces a second source |
| of error... |
| |
| The second problem is that it ``takes a while'' from when an event is |
| dispatched until the profiler's call to get the time actually |
| \emph{gets} the state of the clock. Similarly, there is a certain lag |
| when exiting the profiler event handler from the time that the clock's |
| value was obtained (and then squirreled away), until the user's code |
| is once again executing. As a result, functions that are called many |
| times, or call many functions, will typically accumulate this error. |
| The error that accumulates in this fashion is typically less than the |
| accuracy of the clock (less than one clock tick), but it |
| \emph{can} accumulate and become very significant. This profiler |
| provides a means of calibrating itself for a given platform so that |
| this error can be probabilistically (on the average) removed. |
| After the profiler is calibrated, it will be more accurate (in a least |
| square sense), but it will sometimes produce negative numbers (when |
| call counts are exceptionally low, and the gods of probability work |
| against you :-). ) Do \emph{not} be alarmed by negative numbers in |
| the profile. They should \emph{only} appear if you have calibrated |
| your profiler, and the results are actually better than without |
| calibration. |
| |
| |
| \section{Calibration \label{profile-calibration}} |
| |
| The profiler subtracts a constant from each |
| event handling time to compensate for the overhead of calling the time |
| function, and socking away the results. By default, the constant is 0. |
| The following procedure can |
| be used to obtain a better constant for a given platform (see discussion |
| in section Limitations above). |
| |
| \begin{verbatim} |
| import profile |
| pr = profile.Profile() |
| for i in range(5): |
| print pr.calibrate(10000) |
| \end{verbatim} |
| |
| The method executes the number of Python calls given by the argument, |
| directly and again under the profiler, measuring the time for both. |
| It then computes the hidden overhead per profiler event, and returns |
| that as a float. For example, on an 800 MHz Pentium running |
| Windows 2000, and using Python's time.clock() as the timer, |
| the magical number is about 12.5e-6. |
| |
| The object of this exercise is to get a fairly consistent result. |
| If your computer is \emph{very} fast, or your timer function has poor |
| resolution, you might have to pass 100000, or even 1000000, to get |
| consistent results. |
| |
| When you have a consistent answer, |
| there are three ways you can use it:\footnote{Prior to Python 2.2, it |
| was necessary to edit the profiler source code to embed the bias as |
| a literal number. You still can, but that method is no longer |
| described, because no longer needed.} |
| |
| \begin{verbatim} |
| import profile |
| |
| # 1. Apply computed bias to all Profile instances created hereafter. |
| profile.Profile.bias = your_computed_bias |
| |
| # 2. Apply computed bias to a specific Profile instance. |
| pr = profile.Profile() |
| pr.bias = your_computed_bias |
| |
| # 3. Specify computed bias in instance constructor. |
| pr = profile.Profile(bias=your_computed_bias) |
| \end{verbatim} |
| |
| If you have a choice, you are better off choosing a smaller constant, and |
| then your results will ``less often'' show up as negative in profile |
| statistics. |
| |
| |
| \section{Extensions --- Deriving Better Profilers} |
| \nodename{Profiler Extensions} |
| |
| The \class{Profile} class of module \module{profile} was written so that |
| derived classes could be developed to extend the profiler. The details |
| are not described here, as doing this successfully requires an expert |
| understanding of how the \class{Profile} class works internally. Study |
| the source code of module \module{profile} carefully if you want to |
| pursue this. |
| |
| If all you want to do is change how current time is determined (for |
| example, to force use of wall-clock time or elapsed process time), |
| pass the timing function you want to the \class{Profile} class |
| constructor: |
| |
| \begin{verbatim} |
| pr = profile.Profile(your_time_func) |
| \end{verbatim} |
| |
| The resulting profiler will then call \code{your_time_func()}. |
| The function should return a single number, or a list of |
| numbers whose sum is the current time (like what \function{os.times()} |
| returns). If the function returns a single time number, or the list of |
| returned numbers has length 2, then you will get an especially fast |
| version of the dispatch routine. |
| |
| Be warned that you should calibrate the profiler class for the |
| timer function that you choose. For most machines, a timer that |
| returns a lone integer value will provide the best results in terms of |
| low overhead during profiling. (\function{os.times()} is |
| \emph{pretty} bad, as it returns a tuple of floating point values). If |
| you want to substitute a better timer in the cleanest fashion, |
| derive a class and hardwire a replacement dispatch method that best |
| handles your timer call, along with the appropriate calibration |
| constant. |