Tracking memory allocations

Manually

A quick way to test allocations is to use the @allocated macro, which is available in Base Julia. For example:

a = @allocated begin
    Block to test
end; a > 0 && @show a

That will print something if the code block allocated something.

Using TimerOutputs

One practical tool is TimerOutputs. Install it as usual, and use it with, for example:

using TimerOutputs
const tmr = TimerOutput();

In the code, flag the code lines or blocks with the @timeit macro. For example:

struct A
  x
end
function test(n,x)
    @timeit tmr "set y" y = Vector{A}(undef,n)
    @timeit tmr "loop" for i in 1:n
        @timeit tmr "assign y" y[i] = A(i*x)
    end
    y
end

Running this function fills the tmr object with the time and allocation results:

julia> test(10,rand());

julia> tmr
 ─────────────────────────────────────────────────────────────────────
                              Time                   Allocations      
                      ──────────────────────   ───────────────────────
   Tot / % measured:        176s / 0.00%            103MiB / 0.00%    

 Section      ncalls     time   %tot     avg     alloc   %tot      avg
 ─────────────────────────────────────────────────────────────────────
 loop              2   8.34μs  72.6%  4.17μs   1.14KiB  78.5%     584B
   assign y       20   3.18μs  27.7%   159ns      320B  21.5%    16.0B
 set y             2   3.15μs  27.4%  1.58μs      320B  21.5%     160B
 ─────────────────────────────────────────────────────────────────────

However, @timeit causes some allocations on its own. Therefore, nested calls can cause confusion. This can be verified in the example above. Removing the "assign y" check inside the loop results in:

 ──────────────────────────────────────────────────────────────────
                           Time                   Allocations      
                   ──────────────────────   ───────────────────────
 Tot / % measured:      9.74s / 0.00%           6.00MiB / 0.01%    

 Section   ncalls     time   %tot     avg     alloc   %tot      avg
 ──────────────────────────────────────────────────────────────────
 set y          1   1.29μs  80.6%  1.29μs      160B  50.0%     160B
 loop           1    311ns  19.4%   311ns      160B  50.0%     160B
 ──────────────────────────────────────────────────────────────────

Note that now the loop allocates less. Also, this result is consistent now with the Profile result shown below.

Using the Profiler

To track allocations along the complete code, it is possible to use a profiler, although this generates so much information that it is somewhat confusing. Sometimes the output is not clear either, perhaps even wrong.

For example, consider this is the code (file name here: test.jl):

struct A
    x
end

function test(n,x)
    y = Vector{A}(undef,n)
    for i in 1:n
        y[i] = A(i*x)
    end
    y
end

Run julia with:

julia --track-allocation=user

Within Julia, do:

julia> using Profile

julia> include("./test.jl")
test (generic function with 1 method)

julia> test(10,rand()); # gets compiled

julia> Profile.clear_malloc_data() # clear allocations

julia> test(10,rand());

Exit Julia, this will generate a file test.jl.XXX.mem (extension .mem), which, in this case, contains:

        - struct A
        -     x
        - end
        -
        - function test(n,x)
      160     y = Vector{A}(undef,n)
        0     for i in 1:n
      160       y[i] = A(i*x)
        -     end
        0     y
        - end

Where the lines with non-zero numbers are the lines where allocations occur.

More information: Disabling allocations