Don't understand why code runs out of memory and crashes

General Usage
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22

There is a function task_local_storage() which gives you an IdDict which is local to your task. It can be used to store a vector under some name, e.g. with

v = get!(() -> Vector{Float64}(), task_local_storage(), :myvec)::Vector{Float64}
resize!(v, N)

Here’s a macro which automates away the uglyness, and creates a unique name for the vector:

macro tlscache(type)
    sym = Expr(:quote, gensym("tls"))
    quote
        get!(() -> $(esc(type))(), Base.task_local_storage(), ($sym,$(esc(type))))::$(esc(type))
    end
end

Use as:

x = @tlscache Vector{Float64}
resize!(x, N)
randn!(x)
sort!(x)

By using resize!, the vector is only reallocated if the size N is larger then before. If N is always the same, you allocate only the first time, and you get one vector per task.

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23

Should really use 10^7 instead of messing with floats.

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24

Ran this reproducer and the reproducer from Memory leak with Julia 1.11's GC (discovered in SymbolicRegression.jl) · Issue #56759 · JuliaLang/julia · GitHub with GC.enable_logging. Seems like in both of them, heap size increases, but most of it is mallocd memory (not pool allocated memory), which doesn’t shrink even if you run a GC.

Didn’t investigate further yet to know whether it’s related, but seems suspicious…

See Memory leak with Julia 1.11's GC (discovered in SymbolicRegression.jl) · Issue #56759 · JuliaLang/julia · GitHub

3 Likes
25

That does seem to be it - I can also see in top that it’s the resident memory that increases. Whether this is a true “memory leak” in the sense that the memory is inaccessible to the GC is debatable (presumable it could be freed & is technically reachable by the GC?), but the point that this increase is unexpected holds.

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26

Not a solution to the GC / memory leak issue, but OhMyThreads.jl has a lot of nice functionality for efficient allocations in multithreaded code.

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27

Wow, thanks @gdalle , I had not seen that, already put to good use. There are so many fantastic Julia packages, (that I sadly miss)

d

1 Like
28

I am wondering whether this might be related to this Julia issue:

but I can’t reproduce the OOM with your example. According to the issue thread return nothing from the threaded loop might help in reducing the memory problems.

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