qutip-benchmark

Installation

To run the benchmarks first clone and install QuTiP (this plugin is meant to run with qutip v5 which is not realeased yet).

pip install git+https://github.com/qutip/qutip@dev.major

You will then want to install this repo by cloning it from github.

The benchmarks

The benchmarks so far are two different types, basic operations such as matrix multiplication or addition and solvers.
The operations are benchmarked using 3 parameters:

Density: either sparse or dense, matrices and vectors are created usning qutip.rand_herm(size, density=1/size)for sparse operators and density=1 for dense. Sparse kets ase using qutip.rand_ket(size, density=0.3).
Size: 2**N with N going from 1 to 9. (2 to 512)
dtype (or coeftype fro QobjEvo operations).

Solvers are benchmarked using 2 fixtures:

Size 2**N with N going from 2 to 7. (4 to 128)
The models used are common models such as a cavity, the Jaynes-Cummings model and a qubit spin chain.

Running the benchmarks

to run the benchmarks use the following command from the root of the repository:

python benchmarks/benchmarks.py

This will store the resulting data and figures in the folder .benchmarks/.

The benchmarks consist on a set of operations, such as matrix multiplication, and solvers, such as MeSolve. The benchmarks run the same operations for different hermitian matrix sizes that can either be dense or sparse (tridiagonal). The script also includes a few other options. You can get a description of the arguments with python benchmarks/benchmarks.py --help or see the Pytest documentation and Pytest-Benchmark documetation for all command line flags. Examples:

-python -m qutip_benchmarks.cli.run_benchmarks -k "test_linear_algebra" --collect-only: Shows all the available benchmarks. Useful to filter them with the -k argument.

-python -m qutip_benchmarks.cli.run_benchmarks -k "matmul": Runs only the benchmarks for matmul.

Viewing the benchmarks

The default method to view the benchmarks is by using:

python -m qutip_benchmarks.cli.view_benchmarks

This will plot the benchmarks in an identical manner to what is found on the Qutip's benchmark website.

This scipts accepts 4 flags:

Flag	Description
`--plotpath`	By default separates nightly and scaling into `./images/plots/scaling` and `./images/plots/nightly` if you want them to be separated with a custom path you will need to run this file twice using the `--scaling` and `--nightly` flags and the desired path
`--benchpath`	Path to folder in which the benchmarks are stored
`--scaling`	Only plot scaling (time vs matirx size) from the latest benchmark file
`--nightly`	Plot the performance over time using results from all the benchmark files

If you wish to have more control on what to plot you can import view_utilities.py to a python script and use the available functions. These functions all contain an extensive description of the accepted parameters and outputs can be used for which you can find in benchmarks/view_utilities.py so only a brief description will be given here, you can also view a use case example in this tutorial.

Function	Description
`json_to_dataframe()`	accepts a path to one folder and creates a dataframe with only the information required to produce the plots.
`get_paths()`	accepts the path to the folder containin the results the benchmarks you have run and returns a list of paths to each file conatined within ordered by date.
`create_dataframe()`	accepts a list of paths (produced by `get_paths()`) and creates on big datafram containing all the informaition for the benchmark files.
`sort_ops()`	accepts the dataframe and sort the information by operation and allows you to filter out certain operation if you do not wish to plot them. It outputs a dictionnary with the operation as the key and a dataframe of the corresponding information as the value.
`sort_params()`	accepts the dictionnary produced by `sort_ops()` and sort the data using the fixtures used to perform the benchamrks, such as matrix size or density. You can choose to exclude a certain parameter from the sorting. This effetively separates the information by plot. You can also designate a parameter to be used separate the data into different lines on the same plot. This function outputs a dictionnary with the plot title as a key and the corresponding dataframe and line separators, if any a provided, as the value.
`plot_data()`	Accepts the dictionnary produced by `sort_params()` and creates all the plots, this function also separates the data using a line separator if provided an add a CPU name to the legend, if the benchmarks were performed on VMs or different machines this function will also separete the data into diffrent lines, assigning a color to data separated by a line separator and a diffrent line and marker to the data separated by CPU, as seen on the qutip benchmarks website. You can also choose what should be plotted on the x and y axes, and whether they use logarithmic scaling or not

Support