mpf: Minimal Performance Framework

mpf is a tool to write evaluation experiments, exploring variables of interest to understand the performance of computer systems. mpf can compile code, deploy software to a given computer cluster and orchestrate the evaluation to collect data for which graphs can be generated.

Features

• Experiments are written in IPython.
• Variables can be defined and combined to explore executions scenarios for a given experiment
• Code can be deployed and compiled
• A simple graph is generated from an experiment run

Defining experiments

A mpf experiment consists of an IPython script. It defines several sections, specifying the variables to explore, the roles taking part in the experiments and the specific code they execute. The following example demonstrates how mpf can run an iperf3 server and client and explore values of the -P parallel and -Z zerocopy parameter.

#!/usr/bin/env -S ipython --
import mpf

mpf.default_setup()
mpf.add_variable('parallel', range(1,9))
mpf.add_variable('zerocopy', {'': 'disabled', '-Z': 'enabled'})

@mpf.run(role='server')
def start_server(mpf_ctx):
    %ex iperf3 -D -s -1 > /dev/null

@mpf.run(role='client', delay=1)
def start_client(mpf_ctx, parallel, zerocopy):
    result = %ex iperf3 -f k -t 2 -P $parallel $zerocopy -c {mpf_ctx['roles']['server']['interfaces'][0]['ip']} | tail -n 3 | grep -ioE "[0-9.]+ [kmg]bits"
    return {'goodput': result[0]}

df = next(mpf.run_experiment())

The script defines several parts constituting the experiment. First, it defines two variables that will be explored in the experiment. mpf combines the values of each variable to derive the experiment runs. Second, mpf allows defining functions that will be executed on particular nodes of the cluster. In our example, the function start_server will be executed on the machine bearing the server role. The start_client function will be executed following a one second delay after the previous function completed. This function specifies three parameters. The mpf_ctx object describes the context of the experiment, e.g., the IP addresses of each node. parallel, zerocopy will receive values from the ranges defined above. The start_client collect the iperf3 goodput and returns it as part of the result dictionary.

After the experiment has ran, a pandas Dataframe is returned with the experiment variables composing its indexes and the results its columns. Each row corresponds to one run.

The script can be executed directly and receive a cluster YAML file defining the machines that will run the experiment.

$ tests/experiments/iperf.ipy -c tests/experiments/cluster.yaml

Requirements

Running experiments with mpf requires reasonably coherent Python and packages versions between the executor and the remote nodes. ipyparallel should be installed on the remote nodes and rsync on the node running mpf.