breuner/elbencho repository overview

⁠elbencho

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

⁠Usage Examples

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.

⁠Hello World

To just confirm that fetching of the container works, try showing elbencho's built-in help:

docker run -it breuner/elbencho --help

⁠Simple Benchmark

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

⁠Distributed Mode

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,...

⁠GPUs & GPUDirect Storage (GDS)

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 ....

⁠Docker Image Flavors

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.

⁠S3 Support

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:

• latest
• master-alpine
• master-centos7, master-centos8, master-rocky9
• master-sles15
• master-ubuntu2004, master-ubuntu2204, master-ubuntu2404
• master-ubuntu-cuda-multiarch

⁠Local Image Builds

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 .

⁠ARM64 & Multi-Platform

The image tags "latest" and "master-ubuntu-cuda-multiarch" support amd64 (aka x86_64) and arm64/v8 (aka aarch64) platforms like Nvidia Grace CPUs.