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A quick way to install peanut is with pip:

pip install

(this is a Python 3 package, so you have to change pip by pip3 if your system defaults to Python 2, you may also need to either pass the --user option or prepend this command with sudo)

You need to install peanut on the Grid'5000 frontend of the site you want to use. If you want to launch jobs from your PC, you also need to install it here.


This experiment engine can run several kinds of experiments. As a first example, we will take the HPL benchmark.

First, you need an install file, name it install.yaml, it contains the various options you can use to tune the preamble of this experiment (how HPL is installed, warmup, monitoring...):

trace_execution: False        # True to trace all the MPI and kernel calls
terminate_early: False        # True to terminate the execution after only 5 iterations
insert_bcast: False           # True to insert a small broadcast at the start and end of the main function
trace_dgemm: False            # True to tracc all the dgemm calls
monitoring: 0                 # number of seconds between each probe of the monitoring script, 0 to disable
warmup_time: 600              # number of seconds of the warmup time
openblas: v0.3.13             # string representing the OpenBLAS version to install
openmpi: distribution_package # distribution_package for a 'apt install openmpi', a version string (like "4.1.0") for an installation from source (warning: experimental)

You also need an experiment file, name it exp.csv, each row represent a run of HPL, each column is a parameter (e.g. matrix size, broadcast algorithm...):


Then, from your PC or from the frontend, you can launch a Grid'5000 batch job that will run this experiment on the cluster Dahu with 32 nodes (don't forget to replace my login by yours):

peanut HPL run tocornebize --batch --deploy debian9-x64-base --cluster dahu --nbnodes 32 \
    --walltime 01:00:00 --expfile exp.csv--installfile install.yaml

At the end of the job, you will get a *.zip archive in your Grid'5000 home directory.

Available experiments

A dozen of experiments are currently available in peanut:

  • MPICalibration: calibrate the performance of point-to-point MPI communications
  • MPIRing: advanced calibration method for point-to-point MPI communications, where all the MPI ranks are spamming the MPI_Iprobe function (and optionnally the dgemm function)
  • MPISaturation: run point-to-point MPI communications between an increasing number of node pairs, to estimate the maximum aggregated bandwidth
  • MemoryCalibration: calibrate the performance of memory writes, akin to the MPI calibration script
  • BLASCalibration: calibrate the performance of the dgemm function, either in single-threaded or in multi-threaded mode
  • StressTest: run some basic stress on the nodes (legacy script)
  • HPL: perform real executions of HPL
  • SMPIHPL: perform simulated executions of HPL
  • FrequencyGet: collect and display the core frequency when under stress, with various settings (turboboost, hyperthreading and C-states enabled or not)
  • Simdjson: run some basic benchmarks for the SimdJSON library
  • SW4lite: tentative to run the SW4lite benchmark both in reality and in simulation (unfinished work)
  • BitFlips: tentative to reproduce the "bit-flip" performance annomaly with a custom code (unfinished work)

Adding a new experiment

To add a new experiment, the "best" way is to add a Python file in the peanut directory where a new subclass of peanut.Job is declared, defining the setup and run_exp methods, then add the said class to the classes list in file peanut/

Alternatively, the aforementioned Python file can be directly executed by peanut (provided it contains the subclass of Job with the required methods, as described above):

peanut run tocornebize --deploy debian9-x64-base --cluster dahu --nbnodes 2 --walltime 00:20:00