Parallel GST using MPI

The purpose of this tutorial is to demonstrate how to compute GST estimates in parallel (using multiple CPUs or "processors"). The core PyGSTi computational routines are written to take advantage of multiple processors via the MPI communication framework, and so one must have a version of MPI and the mpi4py python package installed in order use run pyGSTi calculations in parallel.

Since mpi4py doesn't play nicely with Jupyter notebooks, this tutorial is a bit more clunky than the others. In it, we will create a standalone Python script that imports mpi4py and execute it.

We will use as an example the same "standard" single-qubit model of the first tutorial. We'll first create a dataset, and then a script to be run in parallel which loads the data. The creation of a simulated data is performed in the same way as the first tutorial. Since random numbers are generated and used as simulated counts within the call to generate_fake_data, it is important that this is not done in a parallel environment, or different CPUs may get different data sets. (This isn't an issue in the typical situation when the data is obtained experimentally.)

In [1]:
#Import pyGSTi and the "stardard 1-qubit quantities for a model with X(pi/2), Y(pi/2), and idle gates"
import pygsti
from pygsti.modelpacks import smq1Q_XYI

#Create experiment design
exp_design = smq1Q_XYI.get_gst_experiment_design(max_max_length=32), "example_files/mpi_gst_example", clobber_ok=True)

#Simulate taking data
mdl_datagen  = smq1Q_XYI.target_model().depolarize(op_noise=0.1, spam_noise=0.001), "example_files/mpi_gst_example/data/dataset.txt",
                                              nSamples=1000, seed=2020)
<pygsti.objects.dataset.DataSet at 0x11a7f2908>

Next, we'll write a Python script that will load in the just-created DataSet, run GST on it, and write the output to a file. The only major difference between the contents of this script and previous examples is that the script imports mpi4py and passes a MPI comm object (comm) to the do_long_sequence_gst function. Since parallel computing is best used for computationaly intensive GST calculations, we also demonstrate how to set a per-processor memory limit to tell pyGSTi to partition its computations so as to not exceed this memory usage. Lastly, note the use of the gaugeOptParams argument of do_long_sequence_gst, which can be used to weight different model members differently during gauge optimization.

In [2]:
mpiScript = """
import time
import pygsti

#get MPI comm
from mpi4py import MPI

print("Rank %d started" % comm.Get_rank())

#load in data
data ="example_files/mpi_gst_example")

#Specify a per-core memory limit (useful for larger GST calculations)
memLim = 2.1*(1024)**3  # 2.1 GB

#Perform TP-constrained GST
protocol = pygsti.protocols.StandardGST("TP")
start = time.time()
results =, memlimit=memLim, comm=comm)
end = time.time()

print("Rank %d finished in %.1fs" % (comm.Get_rank(), end-start))
if comm.Get_rank() == 0:
    results.write()  #write results (within same diretory as data was loaded from)
with open("example_files/","w") as f:

Next, we run the script with 3 processors using mpiexec. The mpiexec executable should have been installed with your MPI distribution -- if it doesn't exist, try replacing mpiexec with mpirun.

In [3]:
! mpiexec -n 3 python3 "example_files/"
Rank 1 started
Rank 0 started
Rank 2 started
-- Std Practice:  Iter 1 of 1  (TP) --: 
  --- Iterative MLGST: [##################################################] 100.0%  784 operation sequences ---
  Iterative MLGST Total Time: 6.8s
Rank 2 finished in 7.4s
/Users/enielse/pyGSTi/pygsti/algorithms/ UserWarning:

Note: more CPUs(3) than gauge-opt derivative columns(1)!

Rank 0 finished in 7.4s
Rank 1 finished in 7.4s
/Users/enielse/pyGSTi/pygsti/algorithms/ UserWarning:

Note: more CPUs(3) than gauge-opt derivative columns(1)!

/Users/enielse/pyGSTi/pygsti/algorithms/ UserWarning:

Note: more CPUs(3) than gauge-opt derivative columns(1)!

Notice in the above that output within is not duplicated (only the first processor outputs to stdout) so that the output looks identical to running on a single processor. Finally, we just need to read the saved ModelEstimateResults object from file and proceed with any post-processing analysis. In this case, we'll just create a report.

In [4]:
results ="example_files/mpi_gst_example", name="StandardGST")
    results, title="MPI Example Report", verbosity=2
).write_html('example_files/mpi_example_brief', auto_open=True)
Running idle tomography
Computing switchable properties
Found standard clifford compilation from smq1Q_XYI

Open the report.

In [ ]: