Snooping on inference: @snoopi

If you can't use @snoopi_deep due to Julia version constraints, the most useful choice is @snoopi, which is available on Julia 1.2 or higher.

Julia can cache inference results, so you can use @snoopi to generate precompile directives for your package. Executing these directives when the package is compiled may reduce compilation (inference) time when the package is used.

Here's a quick demo:

using SnoopCompile

a = rand(Float16, 5)

julia> inf_timing = @snoopi sum(a)
1-element Array{Tuple{Float64,Core.MethodInstance},1}:
 (0.011293888092041016, MethodInstance for sum(::Array{Float16,1}))

We defined the argument a, and then called sum(a) while "snooping" on inference. (The i in @snoopi means "inference.") The return is a list of "top level" methods that got compiled, together with the amount of time spent on inference. In this case it was just a single method, which required approximately 11ms of inference time. (Inferring sum required inferring all the methods that it calls, but these are subsumed into the top level inference of sum itself.) Note that the method that got called,

julia> @which sum(a)
sum(a::AbstractArray) in Base at reducedim.jl:652

is much more general (i.e., defined for AbstractArray) than the MethodInstance (defined for Array{Float16,1}). This is because precompilation requires the types of the arguments to specialize the code appropriately.

The information obtained from @snoopi can be used in several ways, primarily to reduce latency during usage of your package:

  • to help you understand which calls take the most inference time
  • to help you write precompile directives that run inference on specific calls during package precompilation, so that you don't pay this cost repeatedly each time you use the package
  • to help you identify inference problems that prevent successful or comprehensive precompilation

If you're starting a project to try to reduce latency in your package, broadly speaking there are two paths you can take:

  1. you can use SnoopCompile, perhaps together with CompileBot, to automatically generate lists of precompile directives that may reduce latency;
  2. you can use SnoopCompile primarily as an analysis tool, and then intervene manually to reduce latency.

Beginners often leap at option 1, but experience shows there are good reasons to consider option 2. To avoid introducing too much complexity early on, we'll defer this discussion to the end of this page, but readers who are serious about reducing latency should be sure to read Understanding precompilation and its limitations.


Because invalidations can prevent effective precompilation, developers analyzing their packages with @snoopi are encouraged to use Julia versions (1.6 and higher) that have a lower risk of invalidations in Base and the standard library.

Precompile scripts

You can use @snoopi to come up with a list of precompile-worthy functions. A recommended approach is to write a script that "exercises" the functionality you'd like to precompile. One option is to use your package's "runtests.jl" file, or you can write a custom script for this purpose. Here's an example for the FixedPointNumbers package:

using FixedPointNumbers

x = N0f8(0.2)
y = x + x
y = x - x
y = x*x
y = x/x
y = Float32(x)
y = Float64(x)
y = 0.3*x
y = x*0.3
y = 2*x
y = x*2
y = x/15
y = x/8.0

Save this as a file "snoopfpn.jl" and navigate at the Julia REPL to that directory, and then do

julia> using SnoopCompile

julia> inf_timing = @snoopi tmin=0.01 include("snoopfpn.jl")
2-element Array{Tuple{Float64,Core.MethodInstance},1}:
 (0.03108978271484375, MethodInstance for *(::Normed{UInt8,8}, ::Normed{UInt8,8}))
 (0.04189491271972656, MethodInstance for Normed{UInt8,8}(::Float64))

Here, note the tmin=0.01, which causes any methods that take less than 10ms of inference time to be discarded.


If you're testing this, you might get different results depending on the speed of your machine. Moreover, if FixedPointNumbers has already precompiled these method and type combinations–-perhaps by incorporating a precompile file produced by SnoopCompile–-then those methods will be absent. For packages whose precompile directives are executed only when ccall(:jl_generating_output, Cint, ()) == 1, you can start Julia with --compiled-modules=no to disable them. Alternatively, you can dev the package and comment them out.

You can inspect these results and write your own precompile file, or use the automated tools provided by SnoopCompile.

Producing precompile directives automatically

You can take the output of @snoopi and "parcel" it into packages:

julia> pc = SnoopCompile.parcel(inf_timing)
Dict{Symbol,Array{String,1}} with 1 entry:
  :FixedPointNumbers => ["precompile(Tuple{typeof(*),Normed{UInt8,8},Normed{UInt8,8}})", "precompile(Tuple{Type{Normed{UInt8,8}},Float64})"]

This splits the calls up into a dictionary, pc, indexed by the package which "owns" each call. (In this case there is only one, FixedPointNumbers, but in more complex cases there may be several.) You can then write the results to files:

julia> SnoopCompile.write("/tmp/precompile", pc)

If you look in the /tmp/precompile directory, you'll see one or more files, named by their parent package, that may be suitable for includeing into the package. In this case:

/tmp/precompile$ cat precompile_FixedPointNumbers.jl
function _precompile_()
    ccall(:jl_generating_output, Cint, ()) == 1 || return nothing

If you copy this file to a precompile.jl file in the src directory, you can incorporate it into the package like this:

module FixedPointNumbers

# All the usual commands that define the module go here

# ... followed by:


end # module FixedPointNumbers

The listed method/type combinations should have their inference results cached. Load the package once to precompile it, and then in a fresh Julia session try this:

julia> using SnoopCompile

julia> inf_timing = @snoopi tmin=0.01 include("snoopfpn.jl")
0-element Array{Tuple{Float64,Core.MethodInstance},1}

The fact that no methods were returned is a sign of success: Julia didn't need to call inference on those methods, because it used the inference results from the cache file.


Sometimes, @snoopi will show method & type combinations that you precompiled. This is a sign that despite your attempts, Julia declined to cache the inference results for those methods. You can either delete those directives from the precompile file, or hope that they will become useful in a future version of Julia. Note that having many "useless" precompile directives can slow down precompilation.


As you develop your package, it's possible you'll modify or delete some of the methods that appear in your "precompile.jl" file. This will not result in an error; by default precompile fails silently. If you want to be certain that your precompile directives don't go stale, you can check that precompile returns true and otherwise issue a warning. By default, SnoopCompile.write generates a macro, @warnpcfail, and you can use it by changing precompile(args...) to @warnpcfail precompile(args...).

If you find that some precompile directives are ineffective (they appear in a new @snoopi despite being precompiled) and their inference time is substantial, sometimes a bit of manual investigation of the callees can lead to insights. For example, you might be able to introduce a precompile in a dependent package that can mitigate the total time. (@snoopi_deep makes the analysis and resolution of these issues more straightforward.)


For packages that support just Julia 1.6 and higher, you may be able to slim down the precompile file by adding has_bodyfunction=true to the arguments for parcel. This setting applies for all packges in inf_timing, so you may need to call parcel twice (with both false and true) and select the appropriate precompile file for each package.

Producing precompile directives manually

While this "automated" approach is often useful, sometimes it makes more sense to inspect the results and write your own precompile directives. For example, for FixedPointNumbers a more elegant and comprehensive precompile file might be

function _precompile_()
    ccall(:jl_generating_output, Cint, ()) == 1 || return nothing
    for T in (N0f8, N0f16)      # Normed types we want to support
        for f in (+, -, *, /)   # operations we want to support
            for S in (Float32, Float64, Int)   # other number types we want to support
        for S in (Float32, Float64)

This covers +, -, *, /, and conversion for various combinations of types. The results from @snoopi can suggest method/type combinations that might be useful to precompile, but often you can generalize its suggestions in useful ways.

Analyzing omitted methods

There are some method signatures that cannot be precompiled. For example, suppose you have two packages, A and B, that are independent of one another. Then A.f([B.Object(1)]) cannot be precompiled, because A does not know about B.Object, and B does not know about A.f, unless both A and B get included into a third package.

Such problematic method signatures are removed automatically. If you want to be informed about these removals, you can use Julia's logging framework while running parcel:

julia> using Base.CoreLogging

julia> logger = SimpleLogger(IOBuffer(), CoreLogging.Debug);

julia> pc = with_logger(logger) do

julia> msgs = String(take!(

The omitted method signatures will be logged to the string msgs.

Understanding precompilation and its limitations

Suppose your package includes the following method:

    idx = index_midsum(a)

Return the index of the first item more than "halfway to the cumulative sum,"
meaning the smallest integer so that `sum(a[begin:idx]) >= sum(a)/2`.
function index_midsum(a::AbstractVector)
    ca = cumsum(vcat(0, a))   # cumulative sum of items in a, starting from 0
    s = ca[end]               # the sum of all elements
    return findfirst(x->x >= s/2, ca) - 1  # compensate for inserting 0

Now, suppose that you'd like to reduce latency in using this method, and you know that an important use case is when a is a Vector{Int}. Therefore, you might precompile it:

julia> precompile(index_midsum, (Vector{Int},))

This will cause Julia to infer this method for the given argument types. If you add such statements to your package, it potentially saves your users from having to wait for it to be inferred each time they use your package.


The true indicates that Julia was successfully able to find a method supporting this signature and precompile it. See the note about @warnpcfail above for ways to exploit this in your package.

But if you execute these lines in the REPL, and then check how well it worked, you might see something like the following:

julia> using SnoopCompile

julia> tinf = @snoopi index_midsum([1,2,3,4,100])
3-element Vector{Tuple{Float64, Core.MethodInstance}}:
 (0.00048613548278808594, MethodInstance for cat_similar(::Int64, ::Type, ::Tuple{Int64}))
 (0.010090827941894531, MethodInstance for (::Base.var"#cat_t##kw")(::NamedTuple{(:dims,), Tuple{Val{1}}}, ::typeof(Base.cat_t), ::Type{Int64}, ::Int64, ::Vararg{Any, N} where N))
 (0.016659975051879883, MethodInstance for __cat(::Vector{Int64}, ::Tuple{Int64}, ::Tuple{Bool}, ::Int64, ::Vararg{Any, N} where N))

Even though we'd already said precompile(index_midsum, (Vector{Int},)) in this session, somehow we needed more inference of various concatenation methods. Why does this happen? A detailed investigation (e.g., using Cthulhu or @code_warntype) would reveal that vcat(0, a) is not inferrable "all the way down," and hence the precompile directive couldn't predict everything that was going to be needed.

No problem, you say: let's just precompile those methods too. The most expensive is the last one. You might not know where __cat is defined, but you can find out with

julia> mi = tinf[end][2]    # get the MethodInstance
MethodInstance for __cat(::Vector{Int64}, ::Tuple{Int64}, ::Tuple{Bool}, ::Int64, ::Vararg{Any, N} where N)

julia> mi.def               # get the Method
__cat(A, shape::Tuple{Vararg{Int64, M}}, catdims, X...) where M in Base at abstractarray.jl:1599

julia> mi.def.module        # which module was this method defined in?

When using @snoopi you might sometimes see entries like MethodInstance for (::SomeModule.var"#10#12"{SomeType})(::AnotherModule.AnotherType). These typically correspond to closures/anonymous functions defined with -> or do blocks, but it may not be immediately obvious where these come from. mi.def will show you the file/line number that these are defined on. You can either convert them into named functions to make them easier to precompile, or you can fix inference problems that prevent automatic precompilation (as illustrated below).

Armed with this knowledge, let's start a fresh session (so that nothing is precompiled yet), and in addition to defining index_midsum and precompiling it, we also execute

julia> precompile(Base.__cat, (Vector{Int64}, Tuple{Int64}, Tuple{Bool}, Int, Vararg{Any, N} where N))

Now if you try that tinf = @snoopi index_midsum([1,2,3,4,100]) line, you'll see that the __cat call is omitted, suggesting success.

However, if you copy both precompile directives into your package source files and then check it with @snoopi again, you may be in for a rude surprise: the __cat precompile directive doesn't "work." That turns out to be because your package doesn't "own" that __cat method–the module is Base rather than YourPackage–and because inference cannot determine that it's needed by by index_midsum(::Vector{Int}), Julia doesn't know which *.ji file to use to store its precompiled form.

How to fix this? Fundamentally, the problem is that vcat call: if we can write index_midsum in a way so that inference succeeds, then all these problems go away. (You can use ascend(mi), where mi was obtained above, to discover that __cat gets called from vcat. See ascend for more information.) It turns out that vcat is inferrable if all the arguments have the same type, so just changing vcat(0, a) to vcat([zero(eltype(a))], a) fixes the problem. (Alternatively, you could make a copy and then use pushfirst!.) In a fresh Julia session:

function index_midsum(a::AbstractVector)
    ca = cumsum(vcat([zero(eltype(a))], a))   # cumulative sum of items in a, starting from 0
    s = ca[end]               # the sum of all elements
    return findfirst(x->x >= s/2, ca) - 1  # compensate for inserting 0

julia> precompile(index_midsum, (Vector{Int},))

julia> using SnoopCompile

julia> tinf = @snoopi index_midsum([1,2,3,4,100])
Tuple{Float64, Core.MethodInstance}[]

Tada! No additional inference was needed, ensuring that your users will not suffer any latency due to type-inference of this particular method/argument combination. In addition to identifing a call deserving of precompilation, @snoopi helped us identify a weakness in its implementation. Fixing that weakness reduced latency, made the code more resistant to invalidation, and may improve runtime performance.

In other cases, manual inspection of the results from @snoopi may lead you in a different direction: you may discover that a huge number of specializations are being created for a method that doesn't need them. Typical examples are methods that take types or functions as inputs: for example, there is no reason to recompile methods(f) for each separate f. In such cases, by far your best option is to add @nospecialize annotations to one or more of the arguments of that method. Such changes can have dramatic impact on the latency of your package.

The ability to make interventions like these–which can both reduce latency and improve runtime speed–is a major reason to consider @snoopi primarily as an analysis tool rather than just a utility to blindly generate lists of precompile directives.