Profiling

CPU Profiling

There are two main approaches to CPU profiling julia code:

Via @profile

Where profiling is enabled for a given call via the @profile macro.

  1. julia> using Profile
  3. julia> @profile foo()
  5. julia> Profile.print()
  6. Overhead ╎ [+additional indent] Count File:Line; Function
  7. =========================================================
  8.     ╎147  @Base/client.jl:506; _start()
  9.         ╎ 147  @Base/client.jl:318; exec_options(opts::Base.JLOptions)
  10. ...

Triggered During Execution

Tasks that are already running can also be profiled for a fixed time period at any user-triggered time.

To trigger the profiling:

  • MacOS & FreeBSD (BSD-based platforms): Use ctrl-t or pass a SIGINFO signal to the julia process i.e. % kill -INFO $julia_pid
  • Linux: Pass a SIGUSR1 signal to the julia process i.e. % kill -USR1 $julia_pid
  • Windows: Not currently supported.

First, a single stack trace at the instant that the signal was thrown is shown, then a 1 second profile is collected, followed by the profile report at the next yield point, which may be at task completion for code without yield points e.g. tight loops.

Optionally set environment variable JULIA_PROFILE_PEEK_HEAP_SNAPSHOT to 1 to also automatically collect a heap snapshot.

  1. julia> foo()
  2. ##== the user sends a trigger while foo is running ==##
  3. load: 2.53  cmd: julia 88903 running 6.16u 0.97s
  5. ======================================================================================
  6. Information request received. A stacktrace will print followed by a 1.0 second profile
  7. ======================================================================================
  9. signal (29): Information request: 29
  10. __psynch_cvwait at /usr/lib/system/libsystem_kernel.dylib (unknown line)
  11. _pthread_cond_wait at /usr/lib/system/libsystem_pthread.dylib (unknown line)
  12. ...
  14. ======================================================================
  15. Profile collected. A report will print if the Profile module is loaded
  16. ======================================================================
  18. Overhead ╎ [+additional indent] Count File:Line; Function
  19. =========================================================
  20. Thread 1 Task 0x000000011687c010 Total snapshots: 572. Utilization: 100%
  21.    ╎147 @Base/client.jl:506; _start()
  22.        ╎ 147 @Base/client.jl:318; exec_options(opts::Base.JLOptions)
  23. ...
  25. Thread 2 Task 0x0000000116960010 Total snapshots: 572. Utilization: 0%
  26.    ╎572 @Base/task.jl:587; task_done_hook(t::Task)
  27.       ╎ 572 @Base/task.jl:879; wait()
  28. ...

Customization

The duration of the profiling can be adjusted via Profile.set_peek_duration

The profile report is broken down by thread and task. Pass a no-arg function to Profile.peek_report[] to override this. i.e. Profile.peek_report[] = () -> Profile.print() to remove any grouping. This could also be overridden by an external profile data consumer.

Reference

Profile.@profile — Macro

  1. @profile

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

The methods in Profile are not exported and need to be called e.g. as Profile.print().

Profile.clear — Function

  1. clear()

Clear any existing backtraces from the internal buffer.

Profile.print — Function

  1. print([io::IO = stdout,] [data::Vector = fetch()], [lidict::Union{LineInfoDict, LineInfoFlatDict} = getdict(data)]; 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, :count sorts in order of number of collected samples, and :overhead sorts by the number of samples incurred by each function by itself.

  • groupby – Controls grouping over tasks and threads, or no grouping. Options are :none (default), :thread, :task, [:thread, :task], or [:task, :thread] where the last two provide nested grouping.

  • 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.

  • recur – Controls the recursion handling in :tree format. :off (default) prints the tree as normal. :flat instead compresses any recursion (by ip), showing the approximate effect of converting any self-recursion into an iterator. :flatc does the same but also includes collapsing of C frames (may do odd things around jl_apply).

  • threads::Union{Int,AbstractVector{Int}} – Specify which threads to include snapshots from in the report. Note that this does not control which threads samples are collected on (which may also have been collected on another machine).

  • tasks::Union{Int,AbstractVector{Int}} – Specify which tasks to include snapshots from in the report. Note that this does not control which tasks samples are collected within.

  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 per thread. Each instruction pointer corresponds to a single line of code; backtraces generally consist of a long list of instruction pointers. Note that 6 spaces for instruction pointers per backtrace are used to store metadata and two NULL end markers. Current 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(;include_meta = true) -> data

Return a copy of the buffer of profile backtraces. 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. By default metadata such as threadid and taskid is included. Set include_meta to false to strip metadata.

Profile.retrieve — Function

  1. retrieve(; kwargs...) -> 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.

Profile.get_peek_duration — Function

  1. get_peek_duration()

Get the duration in seconds of the profile “peek” that is triggered via SIGINFO or SIGUSR1, depending on platform.

Profile.set_peek_duration — Function

  1. set_peek_duration(t::Float64)

Set the duration in seconds of the profile “peek” that is triggered via SIGINFO or SIGUSR1, depending on platform.

Memory profiling

Profile.Allocs.@profile — Macro

  1. Profile.Allocs.@profile [sample_rate=0.0001] expr

Profile allocations that happen during expr, returning both the result and and AllocResults struct.

A sample rate of 1.0 will record everything; 0.0 will record nothing.

  1. julia> Profile.Allocs.@profile sample_rate=0.01 peakflops()
  2. 1.03733270279065e11
  4. julia> results = Profile.Allocs.fetch()
  6. julia> last(sort(results.allocs, by=x->x.size))
  7. Profile.Allocs.Alloc(Vector{Any}, Base.StackTraces.StackFrame[_new_array_ at array.c:127, ...], 5576)

The best way to visualize these is currently with the PProf.jl package, by invoking PProf.Allocs.pprof.

Note

The current implementation of the Allocations Profiler does not capture types for all allocations. Allocations for which the profiler could not capture the type are represented as having type Profile.Allocs.UnknownType.

You can read more about the missing types and the plan to improve this, here: https://github.com/JuliaLang/julia/issues/43688.

Julia 1.8

The allocation profiler was added in Julia 1.8.

The methods in Profile.Allocs are not exported and need to be called e.g. as Profile.Allocs.fetch().

Profile.Allocs.clear — Function

  1. Profile.Allocs.clear()

Clear all previously profiled allocation information from memory.

Profile.Allocs.fetch — Function

  1. Profile.Allocs.fetch()

Retrieve the recorded allocations, and decode them into Julia objects which can be analyzed.

Profile.Allocs.start — Function

  1. Profile.Allocs.start(sample_rate::Real)

Begin recording allocations with the given sample rate A sample rate of 1.0 will record everything; 0.0 will record nothing.

Profile.Allocs.stop — Function

  1. Profile.Allocs.stop()

Stop recording allocations.

Heap Snapshots

Profile.take_heap_snapshot — Function

  1. Profile.take_heap_snapshot(io::IOStream, all_one::Bool=false)
  2. Profile.take_heap_snapshot(filepath::String, all_one::Bool=false)
  3. Profile.take_heap_snapshot(all_one::Bool=false)

Write a snapshot of the heap, in the JSON format expected by the Chrome Devtools Heap Snapshot viewer (.heapsnapshot extension), to a file ($pid_$timestamp.heapsnapshot) in the current directory, or the given file path, or IO stream. If all_one is true, then report the size of every object as one so they can be easily counted. Otherwise, report the actual size.

The methods in Profile are not exported and need to be called e.g. as Profile.take_heap_snapshot().

  1. julia> using Profile
  3. julia> Profile.take_heap_snapshot("snapshot.heapsnapshot")

Traces and records julia objects on the heap. This only records objects known to the Julia garbage collector. Memory allocated by external libraries not managed by the garbage collector will not show up in the snapshot.

The resulting heap snapshot file can be uploaded to chrome devtools to be viewed. For more information, see the chrome devtools docs.