JuliaGPU / JuliaGPU/DaggerGPU.jl

CUDA Device Memory Leak

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Description

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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Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Reproduce the issue with the supplied Julia benchmark, comparing run_with_dagger against run_without_dagger and monitoring CUDA device memory across repeated runs. Trace the DaggerGPU/CUDA path used by the Dagger.@spawn calls; done means repeated run_with_dagger executions no longer exhaust device memory.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
distributed-systems, hpc
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
38/100

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