gpu-burn

Multi-GPU CUDA stress test http://wili.cc/blog/gpu-burn.html

• gpu-burn
  • Easy docker build and run
  • Binary packages
  • Building
  • Usage

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 7.5 as specified on NVIDIA's CUDA GPU Compute Capability. To override this with a different value:

make COMPUTE=<compute capability value>

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 <path to host compiler>

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-<version>

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