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Julia 剖析

# Profiling

The Profile module provides tools to help developers improve the performance of their
code. When used, it takes measurements on running code, and produces output that helps you understand
how much time is spent on individual line(s). The most common usage is to identify "bottlenecks"
as targets for optimization.

Profile implements what is known as a "sampling" or statistical profiler.
It works by periodically taking a backtrace during the execution of any task. Each backtrace
captures the currently-running function and line number, plus the complete chain of function calls
that led to this line, and hence is a "snapshot" of the current state of execution.

If much of your run time is spent executing a particular line of code, this line will show up
frequently in the set of all backtraces. In other words, the "cost" of a given line--or really,
the cost of the sequence of function calls up to and including this line--is proportional to how
often it appears in the set of all backtraces.

A sampling profiler does not provide complete line-by-line coverage, because the backtraces occur
at intervals (by default, 1 ms on Unix systems and 10 ms on Windows, although the actual scheduling
is subject to operating system load). Moreover, as discussed further below, because samples are
collected at a sparse subset of all execution points, the data collected by a sampling profiler
is subject to statistical noise.

Despite these limitations, sampling profilers have substantial strengths:

  • You do not have to make any modifications to your code to take timing measurements (in contrast
    to the alternative instrumenting profiler).
  • It can profile into Julia's core code and even (optionally) into C and Fortran libraries.
  • By running "infrequently" there is very little performance overhead; while profiling, your code
    can run at nearly native speed.

For these reasons, it's recommended that you try using the built-in sampling profiler before considering
any alternatives.

# Basic usage

Let's work with a simple test case:

julia> function myfunc()
           A = rand(200, 200, 400)
           maximum(A)
       end

It's a good idea to first run the code you intend to profile at least once (unless you want to
profile Julia's JIT-compiler):

julia> myfunc() # run once to force compilation

Now we're ready to profile this function:

julia> using Profile

julia> @profile myfunc()

To see the profiling results, there is a graphical browser
available, but here we'll use the text-based display that comes with the standard library:

julia> Profile.print()
80 ./event.jl:73; (::Base.REPL.##1#2{Base.REPL.REPLBackend})()
 80 ./REPL.jl:97; macro expansion
  80 ./REPL.jl:66; eval_user_input(::Any, ::Base.REPL.REPLBackend)
   80 ./boot.jl:235; eval(::Module, ::Any)
    80 ./<missing>:?; anonymous
     80 ./profile.jl:23; macro expansion
      52 ./REPL[1]:2; myfunc()
       38 ./random.jl:431; rand!(::MersenneTwister, ::Array{Float64,3}, ::Int64, ::Type{B...
        38 ./dSFMT.jl:84; dsfmt_fill_array_close_open!(::Base.dSFMT.DSFMT_state, ::Ptr{F...
       14 ./random.jl:278; rand
        14 ./random.jl:277; rand
         14 ./random.jl:366; rand
          14 ./random.jl:369; rand
      28 ./REPL[1]:3; myfunc()
       28 ./reduce.jl:270; _mapreduce(::Base.#identity, ::Base.#scalarmax, ::IndexLinear,...
        3  ./reduce.jl:426; mapreduce_impl(::Base.#identity, ::Base.#scalarmax, ::Array{F...
        25 ./reduce.jl:428; mapreduce_impl(::Base.#identity, ::Base.#scalarmax, ::Array{F...

Each line of this display represents a particular spot (line number) in the code. Indentation
is used to indicate the nested sequence of function calls, with more-indented lines being deeper
in the sequence of calls. In each line, the first "field" is the number of backtraces
(samples) taken at this line or in any functions executed by this line.
The second field is the file name and line number and the third field is the function name.
Note that the specific line numbers may change as Julia's
code changes; if you want to follow along, it's best to run this example yourself.

In this example, we can see that the top level function called is in the file event.jl. This is the
function that runs the REPL when you launch Julia. If you examine line 97 of REPL.jl,
you'll see this is where the function eval_user_input() is called. This is the function that evaluates
what you type at the REPL, and since we're working interactively these functions were invoked
when we entered @profile myfunc(). The next line reflects actions taken in the @profile
macro.

The first line shows that 80 backtraces were taken at line 73 of event.jl, but it's not that
this line was "expensive" on its own: the third line reveals that all 80 of these backtraces
were actually triggered inside its call to eval_user_input, and so on. To find out which operations
are actually taking the time, we need to look deeper in the call chain.

The first "important" line in this output is this one:

52 ./REPL[1]:2; myfunc()

REPL refers to the fact that we defined myfunc in the REPL, rather than putting it in a file;
if we had used a file, this would show the file name. The [1] shows that the function myfunc
was the first expression evaluated in this REPL session. Line 2 of myfunc() contains the call to
rand, and there were 52 (out of 80) backtraces that occurred at this line. Below that, you can
see a call to dsfmt_fill_array_close_open! inside dSFMT.jl.

A little further down, you see:

28 ./REPL[1]:3; myfunc()

Line 3 of myfunc contains the call to maximum, and there were 28 (out of 80) backtraces taken
here. Below that, you can see the specific places in base/reduce.jl that carry out the time-consuming
operations in the maximum function for this type of input data.

Overall, we can tentatively conclude that generating the random numbers is approximately twice as expensive
as finding the maximum element. We could increase our confidence in this result by
collecting more samples:

julia> @profile (for i = 1:100; myfunc(); end)

julia> Profile.print()
[....]
 3821 ./REPL[1]:2; myfunc()
  3511 ./random.jl:431; rand!(::MersenneTwister, ::Array{Float64,3}, ::Int64, ::Type...
   3511 ./dSFMT.jl:84; dsfmt_fill_array_close_open!(::Base.dSFMT.DSFMT_state, ::Ptr...
  310  ./random.jl:278; rand
   [....]
 2893 ./REPL[1]:3; myfunc()
  2893 ./reduce.jl:270; _mapreduce(::Base.#identity, ::Base.#scalarmax, ::IndexLinea...
   [....]

In general, if you have N samples collected at a line, you can expect an uncertainty on the
order of sqrt(N) (barring other sources of noise, like how busy the computer is with other tasks).
The major exception to this rule is garbage collection, which runs infrequently but tends to be
quite expensive. (Since Julia's garbage collector is written in C, such events can be detected
using the C=true output mode described below, or by using ProfileView.jl.)

This illustrates the default "tree" dump; an alternative is the "flat" dump, which accumulates
counts independent of their nesting:

julia> Profile.print(format=:flat)
 Count File          Line Function
  6714 ./<missing>     -1 anonymous
  6714 ./REPL.jl       66 eval_user_input(::Any, ::Base.REPL.REPLBackend)
  6714 ./REPL.jl       97 macro expansion
  3821 ./REPL[1]        2 myfunc()
  2893 ./REPL[1]        3 myfunc()
  6714 ./REPL[7]        1 macro expansion
  6714 ./boot.jl      235 eval(::Module, ::Any)
  3511 ./dSFMT.jl      84 dsfmt_fill_array_close_open!(::Base.dSFMT.DSFMT_s...
  6714 ./event.jl      73 (::Base.REPL.##1#2{Base.REPL.REPLBackend})()
  6714 ./profile.jl    23 macro expansion
  3511 ./random.jl    431 rand!(::MersenneTwister, ::Array{Float64,3}, ::In...
   310 ./random.jl    277 rand
   310 ./random.jl    278 rand
   310 ./random.jl    366 rand
   310 ./random.jl    369 rand
  2893 ./reduce.jl    270 _mapreduce(::Base.#identity, ::Base.#scalarmax, :...
     5 ./reduce.jl    420 mapreduce_impl(::Base.#identity, ::Base.#scalarma...
   253 ./reduce.jl    426 mapreduce_impl(::Base.#identity, ::Base.#scalarma...
  2592 ./reduce.jl    428 mapreduce_impl(::Base.#identity, ::Base.#scalarma...
    43 ./reduce.jl    429 mapreduce_impl(::Base.#identity, ::Base.#scalarma...

If your code has recursion, one potentially-confusing point is that a line in a "child" function
can accumulate more counts than there are total backtraces. Consider the following function definitions:

dumbsum(n::Integer) = n == 1 ? 1 : 1 + dumbsum(n-1)
dumbsum3() = dumbsum(3)


If you were to profile `dumbsum3`, and a backtrace was taken while it was executing `dumbsum(1)`,
the backtrace would look like this:


dumbsum3
    dumbsum(3)
        dumbsum(2)
            dumbsum(1)

Consequently, this child function gets 3 counts, even though the parent only gets one. The "tree"
representation makes this much clearer, and for this reason (among others) is probably the most
useful way to view the results.

# Accumulation and clearing

Results from @profile accumulate in a buffer; if you run multiple pieces of code under
@profile, then Profile.print() will show you the combined results. This can
be very useful, but sometimes you want to start fresh; you can do so with Profile.clear().

# Options for controlling the display of profile results

Profile.print has more options than we've described so far. Let's see the full declaration:

function print(io::IO = stdout, data = fetch(); kwargs...)


Let's first discuss the two positional arguments, and later the keyword arguments:

  * `io` -- Allows you to save the results to a buffer, e.g. a file, but the default is to print to `stdout`
    (the console).
  * `data` -- Contains the data you want to analyze; by default that is obtained from [`Profile.fetch()`](@ref),
    which pulls out the backtraces from a pre-allocated buffer. For example, if you want to profile
    the profiler, you could say:


    data = copy(Profile.fetch())
    Profile.clear()
    @profile Profile.print(stdout, data) # Prints the previous results
    Profile.print()                      # Prints results from Profile.print()

The keyword arguments can be any combination of:

  • format -- Introduced above, 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). Try running the introductory
    example with Profile.print(C = true). This can be extremely helpful in deciding whether it's
    Julia code or C code that is causing a bottleneck; setting C = true also improves the interpretability
    of the nesting, at the cost of longer profile dumps.
  • combine -- Some lines of code contain multiple operations; for example, s += A[i] contains both an array
    reference (A[i]) and a sum operation. These correspond to different lines in the generated
    machine code, and hence there may be two or more different addresses captured during backtraces
    on this line. combine = true lumps them together, and is probably what you typically want, but
    you can generate an output separately for each unique instruction pointer with combine = false.
  • maxdepth -- Limits frames at a 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 are below 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 frames with less than mincount occurrences.

File/function names are sometimes truncated (with ...), and indentation is truncated with a
+n at the beginning, where n is the number of extra spaces that would have been inserted,
had there been room. If you want a complete profile of deeply-nested code, often a good idea is
to save to a file using a wide displaysize in an IOContext:

open("/tmp/prof.txt", "w") do s
Profile.print(IOContext(s, :displaysize => (24, 500)))
end


## Configuration

[`@profile`](@ref) just accumulates backtraces, and the analysis happens when you call [`Profile.print()`](@ref).
For a long-running computation, it's entirely possible that the pre-allocated buffer for storing
backtraces will be filled. If that happens, the backtraces stop but your computation continues.
As a consequence, you may miss some important profiling data (you will get a warning when that
happens).

You can obtain and configure the relevant parameters this way:


Profile.init() # returns the current settings
Profile.init(n = 10^7, delay = 0.01)

n is the total number of instruction pointers you can store, with a default value of 10^6.
If your typical backtrace is 20 instruction pointers, then you can collect 50000 backtraces, which
suggests a statistical uncertainty of less than 1%. This may be good enough for most applications.

Consequently, you are more likely to need to modify delay, expressed in seconds, which sets
the amount of time that Julia gets between snapshots to perform the requested computations. A
very long-running job might not need frequent backtraces. The default setting is delay = 0.001.
Of course, you can decrease the delay as well as increase it; however, the overhead of profiling
grows once the delay becomes similar to the amount of time needed to take a backtrace (~30 microseconds
on the author's laptop).

# Memory allocation analysis

One of the most common techniques to improve performance is to reduce memory allocation. The
total amount of allocation can be measured with @time and @allocated, and
specific lines triggering allocation can often be inferred from profiling via the cost of garbage
collection that these lines incur. However, sometimes it is more efficient to directly measure
the amount of memory allocated by each line of code.

To measure allocation line-by-line, start Julia with the --track-allocation=<setting> command-line
option, for which you can choose none (the default, do not measure allocation), user (measure
memory allocation everywhere except Julia's core code), or all (measure memory allocation at
each line of Julia code). Allocation gets measured for each line of compiled code. When you quit
Julia, the cumulative results are written to text files with .mem appended after the file name,
residing in the same directory as the source file. Each line lists the total number of bytes
allocated. The Coverage package contains some elementary
analysis tools, for example to sort the lines in order of number of bytes allocated.

In interpreting the results, there are a few important details. Under the user setting, the
first line of any function directly called from the REPL will exhibit allocation due to events
that happen in the REPL code itself. More significantly, JIT-compilation also adds to allocation
counts, because much of Julia's compiler is written in Julia (and compilation usually requires
memory allocation). The recommended procedure is to force compilation by executing all the commands
you want to analyze, then call Profile.clear_malloc_data() to reset all allocation counters.
Finally, execute the desired commands and quit Julia to trigger the generation of the .mem
files.

# External Profiling

Currently Julia supports Intel VTune, OProfile and perf as external profiling tools.

Depending on the tool you choose, compile with USE_INTEL_JITEVENTS, USE_OPROFILE_JITEVENTS and
USE_PERF_JITEVENTS set to 1 in Make.user. Multiple flags are supported.

Before running Julia set the environment variable ENABLE_JITPROFILING to 1.

Now you have a multitude of ways to employ those tools!
For example with OProfile you can try a simple recording :

>ENABLE_JITPROFILING=1 sudo operf -Vdebug ./julia test/fastmath.jl
>opreport -l `which ./julia`

Or similary with with perf :

$ ENABLE_JITPROFILING=1 perf record -o /tmp/perf.data --call-graph dwarf ./julia /test/fastmath.jl
$ perf report --call-graph -G

There are many more interesting things that you can measure about your program, to get a comprehensive list
please read the Linux perf examples page.

Remember that perf saves for each execution a perf.data file that, even for small programs, can get
quite large. Also the perf LLVM module saves temporarily debug objects in ~/.debug/jit, remember
to clean that folder frequently.