breuner/elbencho
A distributed benchmark for file, object & block storage with support for GPUs
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A distributed benchmark for file, object & block storage with support for GPUs
For the full description, see elbencho's readme and changelog on github: https://github.com/breuner/elbencho
The default dockerfile defines the path to the elbencho executable as an entrypoint. This means you don't need to specify the name or path of the elbencho binary and just directly provide the elbencho parameters to the docker run
command.
To just confirm that fetching of the container works, try showing elbencho's built-in help:
docker run -it breuner/elbencho --help
Use docker's -v HOST_PATH:CONTAINER_PATH
option to make your test directory available inside the container. E.g. if your benchmark files should be in /data
and you want to create a single 10GiB file:
docker run -v /data:/data -it breuner/elbencho /data/mytestfile -s 10G -w
If you want to use elbencho's distributed mode for coordinated throughput tests from multiple nodes, the easiest way is to use Docker's --net=host
option to make the elbencho service inside the container available under the IP address of the host on which it is running. Also, you will want to prevent the service from going into background (because otherwise Docker will think it can stop the container instance) and instead detach the container to run in the background (--foreground
& -d
).
Run this command on all nodes that should participate in your distributed storage benchmarks:
docker run -v /data:/data --net=host -dt breuner/elbencho --service --foreground
Now you're ready to control the service instances, again by using the --net=host
option for the master instance. Here we write a single shared 10GiB file from all service instances:
docker run --net=host -it breuner/elbencho /data/mytestfile -s 10G -w --hosts HOST1,HOST2,...
When you're done with your distributed benchmarks and want to stop the service instances, use elbencho's --quit
option to terminate them:
docker run --net=host -it breuner/elbencho --quit --hosts HOST1,HOST2,...
To test GPU storage access performance through Nvidia CUDA or GPUDirect Storage (GDS/cuFile), use the multi-arch Ubuntu container with CUDA installed (tag "master-ubuntu-cuda-multiarch").
Here is an example to write and read 128 large file via GDS, using 256 threads and all available GPUs in the host:
nvidia-docker run --privileged -v /data:/data -it breuner/elbencho:master-ubuntu-cuda-multiarch "/data/mylargefile[1..128]" -w -r -t 256 -s 12g -b 4m --direct --gpuids all --gds
If you don't have nvidia-docker
, you can alternatively use docker run --gpus all ...
.
The default image (tagged "latest") is based on Ubuntu 24.04.
The Ubuntu and CentOS based images contain the full .deb
/ .rpm
package installation. To use e.g. one of the contained elbencho tools, simply specify the tool name as an alternative entrypoint:
docker run -it --entrypoint elbencho-scan-path breuner/elbencho:master-ubuntu2404 --help
The Alpine Linux based image is optimized for minimum size and only contains the elbencho main executable, no other tools.
Images with stable version tags include S3 support as of elbencho v2.0. Additionally, the following image tags based on the master branch include S3 support:
To build docker images from your local elbencho git clone (e.g. because you modified the sources or want to build a particular previous version), you can find the corresponding dockerfiles in the build_helpers/docker
subdir with a .local
extension. To build a Ubuntu 22.04 image from your local sources, run this command:
docker build -t elbencho-local -f build_helpers/docker/Dockerfile.ubuntu2404.local .
The image tag "master-ubuntu-cuda-multiarch" supports x86_64 and arm64/v8 (aka aarch64) platforms like Nvidia Grace CPUs.
docker pull breuner/elbencho