性能分析

Profile.@profile — Macro

  1. @profile

@profile <expression> runs your expression while taking periodic backtraces. These are appended to an internal buffer of backtraces.

Profile 里的方法均未导出，需要通过 Profile.print() 的方式调用。

Profile.clear — Function

  1. clear()

Clear any existing backtraces from the internal buffer.

Profile.print — Function

  1. print([io::IO = stdout,] [data::Vector]; kwargs...)

Prints profiling results to io (by default, stdout). If you do not supply a data vector, the internal buffer of accumulated backtraces will be used.

The keyword arguments can be any combination of:

  • format – Determines whether backtraces are printed with (default, :tree) or without (:flat) indentation indicating tree structure.

  • C – If true, backtraces from C and Fortran code are shown (normally they are excluded).

  • combine – If true (default), instruction pointers are merged that correspond to the same line of code.

  • maxdepth – Limits the depth higher than maxdepth in the :tree format.

  • sortedby – Controls the order in :flat format. :filefuncline (default) sorts by the source line, whereas :count sorts in order of number of collected samples.

  • noisefloor – Limits frames that exceed the heuristic noise floor of the sample (only applies to format :tree). A suggested value to try for this is 2.0 (the default is 0). This parameter hides samples for which n <= noisefloor * √N, where n is the number of samples on this line, and N is the number of samples for the callee.

  • mincount – Limits the printout to only those lines with at least mincount occurrences.

  1. print([io::IO = stdout,] data::Vector, lidict::LineInfoDict; kwargs...)

Prints profiling results to io. This variant is used to examine results exported by a previous call to retrieve. Supply the vector data of backtraces and a dictionary lidict of line information.

See Profile.print([io], data) for an explanation of the valid keyword arguments.

Profile.init — Function

  1. init(; n::Integer, delay::Real))

Configure the delay between backtraces (measured in seconds), and the number n of instruction pointers that may be stored. Each instruction pointer corresponds to a single line of code; backtraces generally consist of a long list of instruction pointers. Default settings can be obtained by calling this function with no arguments, and each can be set independently using keywords or in the order (n, delay).

Profile.fetch — Function

  1. fetch() -> data

Returns a reference to the internal buffer of backtraces. Note that subsequent operations, like clear, can affect data unless you first make a copy. Note that the values in data have meaning only on this machine in the current session, because it depends on the exact memory addresses used in JIT-compiling. This function is primarily for internal use; retrieve may be a better choice for most users.

Profile.retrieve — Function

  1. retrieve() -> data, lidict

“Exports” profiling results in a portable format, returning the set of all backtraces (data) and a dictionary that maps the (session-specific) instruction pointers in data to LineInfo values that store the file name, function name, and line number. This function allows you to save profiling results for future analysis.

Profile.callers — Function

  1. callers(funcname, [data, lidict], [filename=<filename>], [linerange=<start:stop>]) -> Vector{Tuple{count, lineinfo}}

Given a previous profiling run, determine who called a particular function. Supplying the filename (and optionally, range of line numbers over which the function is defined) allows you to disambiguate an overloaded method. The returned value is a vector containing a count of the number of calls and line information about the caller. One can optionally supply backtrace data obtained from retrieve; otherwise, the current internal profile buffer is used.

Profile.clear_malloc_data — Function

  1. clear_malloc_data()

Clears any stored memory allocation data when running julia with --track-allocation. Execute the command(s) you want to test (to force JIT-compilation), then call clear_malloc_data. Then execute your command(s) again, quit Julia, and examine the resulting *.mem files.