A Nextflow pipeline for lightweight benchmarking. Provides a simple way to obtain a "fingerprint" of a platform based on benchmarking metrics. Can be used with tesseract to predict resource usage across multiple platforms or multiple node types within a platform.

Run minibench on whatever platform to collect benchmarking metrics for every node type on that platform. The only thing you need to do beforehand is to create a profile in nextflow.config that defines the node types on your platform. The config file currently has a few examples for a local machine, the Palmetto cluster at Clemson University, the Nautilus cluster, and Google Cloud.

To run minibench on a Kubernetes cluster:

nextflow kuberun bentsherman/minibench -v <pvc-name>

To run minibench on Google Cloud:

# define environment variables
export GOOGLE_APPLICATION_CREDENTIALS="<credentials-file>"
export NXF_MODE="google"

# run pipeline
nextflow run bentsherman/minibench \
    -profile google \
    --google_bucket <bucket>/work \
    --google_zone <zone>

# transfer work directory from cloud storage
gsutil -m cp -r gs:https://nextflow-data/work .
gsutil -m rm -r gs:https://nextflow-data/work

You can then use tesseract to collect the input features (i.e. node_type) and benchmarking metrics into a dataframe. Refer to the tesseract repo for more information.

In order to use the benchmarking metrics as input features in a trace dataset for another Nextflow pipeline, each task must be tagged with the node_type on which it was executed, so that the benchmarking metrics can be properly merged with the application trace data. The easiest way to do this is to create a config file that inserts a trace directive for node_type. Refer to the conf folder for examples.

To run KINC-nf with the minibench shim on Palmetto:

nextflow run -C minibench/palmetto.config systemsgenetics/kinc-nf -profile palmetto,singularity