https://github.com/wilicc/gpu-burn

Multi-GPU CUDA stress test
https://github.com/wilicc/gpu-burn

Last synced: 2 days ago
JSON representation

Multi-GPU CUDA stress test

• Host: GitHub
• URL: https://github.com/wilicc/gpu-burn
• Owner: wilicc
• License: bsd-2-clause
• Created: 2017-11-25T07:45:02.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-08-20T12:15:53.000Z (6 months ago)
• Last Synced: 2025-01-30T11:32:04.811Z (9 days ago)
• Language: C++
• Size: 70.3 KB
• Stars: 1,527
• Watchers: 18
• Forks: 305
• Open Issues: 53
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-cuda-triton-hpc - wilicc/gpu-burn - burn?style=social"/> : Multi-GPU CUDA stress test. (Frameworks)
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README

        
# gpu-burn

Multi-GPU CUDA stress test

http://wili.cc/blog/gpu-burn.html

# Easy docker build and run

```

git clone https://github.com/wilicc/gpu-burn

cd gpu-burn

docker build -t gpu_burn .

docker run --rm --gpus all gpu_burn

```

# Binary packages

https://repology.org/project/gpu-burn/versions

# Building

To build GPU Burn:

`make`

To remove artifacts built by GPU Burn:

`make clean`

GPU Burn builds with a default Compute Capability of 5.0.

To override this with a different value:

`make COMPUTE=`

CFLAGS can be added when invoking make to add to the default

list of compiler flags:

`make CFLAGS=-Wall`

LDFLAGS can be added when invoking make to add to the default

list of linker flags:

`make LDFLAGS=-lmylib`

NVCCFLAGS can be added when invoking make to add to the default

list of nvcc flags:

`make NVCCFLAGS=-ccbin `

CUDAPATH can be added to point to a non standard install or

specific version of the cuda toolkit (default is 

/usr/local/cuda):

`make CUDAPATH=/usr/local/cuda-`

CCPATH can be specified to point to a specific gcc (default is

/usr/bin):

`make CCPATH=/usr/local/bin`

CUDA_VERSION and IMAGE_DISTRO can be used to override the base

images used when building the Docker `image` target, while IMAGE_NAME

can be set to change the resulting image tag:

`make IMAGE_NAME=myregistry.private.com/gpu-burn CUDA_VERSION=12.0.1 IMAGE_DISTRO=ubuntu22.04 image`

# Usage

    GPU Burn

    Usage: gpu_burn [OPTIONS] [TIME]

    

    -m X   Use X MB of memory

    -m N%  Use N% of the available GPU memory

    -d     Use doubles

    -tc    Try to use Tensor cores (if available)

    -l     List all GPUs in the system

    -i N   Execute only on GPU N

    -h     Show this help message

    

    Example:

    gpu_burn -d 3600