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CUDA Device Memory Leak #30

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RomeoV opened this issue Dec 13, 2023 · 3 comments
Open

CUDA Device Memory Leak #30

RomeoV opened this issue Dec 13, 2023 · 3 comments

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@RomeoV
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RomeoV commented Dec 13, 2023 •
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I tried to create a small benchmark to see if DaggerGPU.jl can help me asynchronously move data to and from the GPU.
However, it seems that the memory on the CUDA device is not free'd properly, and so I'm always running out of memory on the device.

Here's the benchmark

# after creating the environment, run e.g. with julia --project=bench -p 4
using Distributed, BenchmarkTools
import LinearAlgebra: tr
@everywhere using Dagger, CUDA, DaggerGPU
A_matrices = [rand(1_000, 1_000) for _ in 1:100];
B_gpu = CUDA.rand(1_000, 1_000)
sc = Dagger.scope(cuda_gpu=1)

function run_with_dagger()
    C_results = []
    for A in A_matrices
        A_gpu = Dagger.@spawn scope=sc CUDA.CuMatrix(A)
        C_gpu = Dagger.@spawn scope=sc A_gpu*B_gpu
        C_cpu = Dagger.@spawn scope=sc Matrix(C_gpu)
        push!(C_results, C_cpu)
    end
    traces = collect(tr(fetch(C)) for C in C_results)
    return nothing
end

function run_without_dagger()
    C_results = []
    for A in A_matrices
        A_gpu = CUDA.CuMatrix(A)
        C_gpu = A_gpu*B_gpu
        C_cpu = Matrix(C_gpu)
        push!(C_results, C_cpu)
    end
    traces = collect(tr(fetch(C)) for C in C_results)
    return nothing
end

I can run

[run_without_dagger() for _ in 1:100]
# or 
@benchmark run_without_dagger()

just fine, and I can also run

run_with_dagger()

once, but when I run

[run_with_dagger() for _ in 1:100]
# or 
@benchmark run_without_dagger()

the device memory quickly fills up and the program crashes.
It seems to me that the device memory is not free'd properly.

versioninfo, Project.toml and Manifest.toml

versioninfo

Julia Version 1.10.0-rc2
Commit dbb9c46795b (2023-12-03 15:25 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 16 × 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-15.0.7 (ORCJIT, tigerlake)
Threads: 1 on 16 virtual cores

Project.toml

[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
Dagger = "d58978e5-989f-55fb-8d15-ea34adc7bf54"
DaggerGPU = "68e73e28-2238-4d5a-bf97-e5d4aa3c4be2"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"

Manifest.toml

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project_hash = "3a5ae414e436e4b59c0ce86c5f69f14091e3dcea"

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AbstractFFTsTestExt = "Test"

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@RomeoV
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RomeoV commented Dec 13, 2023

The error still persists if replace the failing call with a more pessimistic

for i in 1:100
  CUDA.@sync run_with_dagger()
  GC.gc(true)
end

which further points at a memory leak.

@RomeoV
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RomeoV commented Dec 13, 2023

Actually, it turns out the memory leak only happens when I launch with multiple processes (i.e. Distributed).
If I don't everything runs fine.

Probably the GC has trouble when the GPU arrays are owned by different processes...

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RomeoV commented Dec 13, 2023

I was hoping that binding the spawn commands to the main worker would fix the problem, but it doesn't seem so.

Specifically, calling with julia --project=bench -p4 and then setting the scope to sc = Dager.scope(worker=myid(), cuda_gpu=1) doesn't seem to alleviate the memory leak.

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