piquassoboost 0.3.5

The C++ binding for the Piquasso project

Piquasso Boost

The Piquasso Boost library intends to improve the performance and the scaleability of the computationally most demanding components of the Piquasso bosonic quantum simulation package. The Piquasso Boost library is written in C/C++ providing a Python interface via C++ extensions. The library is equipped with the Threading Building Block (TBB) library providing an efficient task oriented parallel programming model to achieve an optimal workload balance among the accessible execution units of the underlying hardware avoiding any over-subscription of the resources. Thus, the parallelized components of the Piquasso Boost library can be freely combined with each other without the cost of performance drop-down.

The Piquasso Boost library utilizes recently developed algorithms to ensure the most favorable scaling of the number of the floating point operations (FLOPS) with the problem size. In order to reduce the computational time to the minimum we designed the structure of the code to also keep the number of memory operations (MEMOPS) as low as possible by the reuse of the data already loaded into the cache-line hierarchy of the CPU units whenever it is possible. The register level parallelism via portable SIMD instruction are provided by the implementation of low level BLAS kernels in calculations involving double precision floating point representation.

When it comes to large scaled problems (for example boson sampling simulations involving 20-30 or more photons) the necessary computational precision is ensured by a mixture of double and extended precision floating point operations while keeping the running time as low as possible. The numerical stability of the library obtained by mixing different precision floating point representations is governed by heuristically determined internal parameters. Also, the interplay of MPI and TBB parallel libraries implemented in the Piquasso Boost library (the compilation of the library with MPI support is optional) provides a high scaleability in HPC environments allowing to spawn heavy computational tasks across several cluster nodes.

The present package is supplied with Python building script and CMake tools to ease its deployment. The Piquasso Boost library package can be built with both Intel and GNU compilers, and can be link against various CBLAS libraries installed on the system. (So far OpenBLAS and the Intel MKL packages were tested.) In the following we briefly summarize the steps to build, install and use the Piquasso Boost library.

Installation

The intended end-user installation path is:

$ pip install piquassoboost

The piquassoboost package name has been reserved for the upcoming PyPI release. Until the first release is published, or on platforms where a prebuilt wheel is not yet available, install from source using the manual build steps below.

On Linux, Piquasso Boost depends on piquasso==7.2.1, which is installed automatically by pip.

The project was supported by the Ministry of Innovation and Technology and the National Research, Development and Innovation Office within the Quantum Information National Laboratory of Hungary.

Citation

If you use Piquasso Boost in your research, please cite:

Morse, Gregory; Rybotycki, Tomasz; Kaposi, Ágoston; Kolarovszki, Zoltán; Stojčić, Uros; Kozsik, Tamás; Mencer, Oskar; Oszmaniec, Michał; Zimborás, Zoltán; Rakyta, Péter. High performance Boson sampling simulation via data-flow engines. New Journal of Physics 26(3), 033033 (2024).

• DOI: https://doi.org/10.1088/1367-2630/ad313b
• PDF: https://iopscience.iop.org/article/10.1088/1367-2630/ad313b/pdf

For the Piquasso photonic quantum computing framework that Piquasso Boost accelerates, please also cite:

Kolarovszki, Zoltán; Rybotycki, Tomasz; Rakyta, Péter; Kaposi, Ágoston; Poór, Boldizsár; Jóczik, Szabolcs; Nagy, Dániel T. R.; Varga, Henrik; El-Safty, Kareem H.; Morse, Gregory; Oszmaniec, Michał; Kozsik, Tamás; Zimborás, Zoltán. Piquasso: A Photonic Quantum Computer Simulation Software Platform. Quantum 9, 1708 (2025).

• DOI: https://doi.org/10.22331/q-2025-04-15-1708
• arXiv: https://arxiv.org/abs/2403.04006

For the recursive Torontonian and loop-Torontonian algorithms, please also cite:

Kaposi, Ágoston; Kolarovszki, Zoltán; Kozsik, Tamás; Zimborás, Zoltán; Rakyta, Péter. Polynomial speedup in Torontonian calculation by a scalable recursive algorithm (2021).

• arXiv: https://arxiv.org/abs/2109.04528
• DOI: https://doi.org/10.48550/arXiv.2109.04528

Related talks and posters

The following conference talks and poster presentations are directly related to Piquasso and/or Piquasso Boost:

• GPU Day 2021 (Nov 10-11, 2021, Budapest, Hungary)

  • Peter Rakyta, "Boson sampling simulation enhanced by FPGA based data-flow engines" (Nov 11, 10:20-10:40).
  • Event page: https://gpuday.com/gpu-day-2021

• GPU Day 2022 (Jun 20-21, 2022, Budapest, Hungary)

  • Zoltan Zimboras, "Piquasso, a comprehensive framework for optical quantum computer programming and simulation" (Jun 21, 9:00-9:30).
  • Gregory Morse, "Custom Tailored FPGA Boson Sampling" (Jun 21, 9:50-10:10).
  • Agoston Kaposi, "Polynomial speedup in exact Torontonian calculation by a scalable recursive algorithm" (Jun 21, 11:20-11:40).
  • Event page: https://gpuday.com/gpu-day-2022

• QT4HEP22 (International Conference on Quantum Technologies for High-Energy Physics) (Nov 1-4, 2022, CERN)

  • Poster: "Piquasso: A Photonic Quantum Computer Simulation Software Platform".
  • Speaker: Zoltan Kolarovszki.
  • Authors/co-authors include: Zoltan Kolarovszki, Zoltan Zimboras, Peter Rakyta, Agoston Kaposi, Szabolcs Joczki, Boldizsar Poor, Tamas Kozsik.
  • Contribution page: https://indico.cern.ch/event/1190278/contributions/5107367/

• QIP 2023 (Quantum Information Processing 2023) (Feb 4-10, 2023, Ghent, Belgium)

  • Poster session (Tuesday): "Piquasso: A Photonic Quantum Computer Simulation Software Platform".
  • Authors: Zoltan Kolarovszki, Tomasz Rybotycki, Peter Rakyta, Agoston Kaposi, Boldizsar Poor, Szabolcs Joczki, Kareem H. El-Safty, Gregory Morse, Gabor Nemeth, Daniel Nagy, Zsofia Kallus, Michal Oszmaniec, Tamas Kozsik, Zoltan Zimboras.
  • Session time: Feb 7, 2023, 19:00-21:00.
  • Session page: https://indico.global/event/13076/page/3897-tuesday-session

• GPU Day 2023 (May 15-16, 2023, Budapest, Hungary)

  • Zoltan Kolarovszki, "Automatic differentiation of photonic quantum circuits" (May 15, 14:00-14:25).
  • Peter Rakyta, "Simulation of quantum computers on tensor streaming processors" (May 16, 9:00-9:25).
  • Event page: https://gpuday.com/gpu-day-2023

• GPU Day 2024 (May 30-31, 2024, Budapest, Hungary)

  • Zoltan Kolarovszki, "Simulation of photonic quantum computing with automatic differentiation in Piquasso" (May 30, 11:00-11:25).
  • Event page: https://gpuday.com/gpu-day-2024

• APS March Meeting 2024 (Mar 4-8, 2024, Minneapolis & Virtual)

  • Talk (Session D53.00012): "Piquasso: A Photonic Quantum Computer Simulation Software Platform".
  • Presenter: Zoltan Kolarovszki.
  • Authors include: Zoltan Kolarovszki, Peter Rakyta, Tamas Kozsik, Zoltan Zimboras.
  • Abstract page: https://meetings.aps.org/Meeting/MAR24/Session/D53.12

Related (non-conference) article:

• Peter Rakyta (ELTE), Agoston Kaposi, Zoltan Kolarovszki, Tamas Kozsik (ELTE), and Zoltan Zimboras (Wigner), "Simulation of Photonic Quantum Computers Enhanced by Data-Flow Engines," ERCIM News 128 (special theme article).
  • https://ercim-news.ercim.eu/en128/special/simulation-of-photonic-quantum-computers-enhanced-by-data-flow-engines

Contact Us

Have a question about the Piquasso Boost library? Don't hesitate to contact us by creating a new issue or directly at the following e-mails:

• Zoltán Zimborás (researcher): [e-mail]
• Zoltán Kolarovszki (developer): [e-mail]
• Kaposi Ágoston (developer): [e-mail]
• Peter Rakyta (developer): [e-mail]
• Gregory Morse (developer): [e-mail]

Dependencies

The dependencies necessary to compile and build the Piquasso Boost library from source are the followings:

• CMake (>=3.10.2)
• C++/C GNU (>=v4.8.1) or Intel (>=14.0.1) compiler
• TBB library
• OpenBlas or Intel MKL
• LAPACK
• LAPACKE
• MPI library (optional)
• Doxygen (optional)

The Python interface of the Piquasso Boost library needs the following packages to be installed on the system:

• scikit-build (>=0.11.1)
• Numpy (>=1.19.4)
• scipy (>=1.5.2)
• quantum-blackbird (==0.2.3)
• theboss (>=2.0.3)
• tbb-devel
• mpi4py
• pytest
• piquasso

Note: In some distributions, OpenBLAS might not come with CBLAS, it might be needed to install CBLAS manually.

Source build

Use the source build path for local development, editable installs, unpublished branches, or platforms where pip install piquassoboost needs to build from source.

Download the source of the Piquasso Boost library

The developer version of the Piquasso Boost library can be cloned from GitHub repository. After the Piquasso Boost repository is extracted into the directory path/to/piquasso_boost/library (which would be the path to the source code of the Piquasso Boost library), one can proceed to the compilation steps described in the next section.

Initialize Piquasso submodule

In case the Piquasso Boost library was cloned from GitHub repositories, the first step is to activate the piquasso submodule by git commands (The piquasso submodule provides the high level Python API of the Piquasso project.):

$ git submodule init

$ git submodule update

The commands above initialize and pull down the piquasso submodule from GitHub sources.

Setting up environment variables

The Piquasso Boost library is equipped with a Python build script and CMake tools to ease the compilation and the deployment of the package. These scripts automatically finds all library dependencies needed to compile Piquasso Boost. The Piquasso Boost library is parallelized via Threading Building Block (TBB) libraries. The most straightforward way to get TBB development package installed on the system is to install the python package tbb-devel containing the most recent version of the TBB library (including the header files). (The tbb-devel package can be installed in any python virtual environment, thus it is not needed to have administration privileges to have it.)
If the TBB library is already present on the system (for example it was installed via the apt utility (sudo apt install libtbb-dev) or it was downloaded and built from source from https://github.com/oneapi-src/oneTBB) and the user wants to use this version of the TBB library, it is possible by (optionally) setting the

$ export TBB_LIB_DIR=path/to/TBB/lib(64)

$ export TBB_INC_DIR=path/to/TBB/include

environment variables. The building script will look for TBB libraries and header files on the paths given by these environment variables.

CBLAS and LAPACK libaraies are another dependencies necessary to use the Piquasso Boost library. Since it is advised to have numpy linked against such a library (for example anaconda automatically brings numpy linked against Intel MKL or OpenBLAS) the building script will automatically find out the location of this library. (To check whether there is any CBLAS libarary behind numpy use commands import numpy as np and np.show_config() inside a python interpreter and check the given library locations.) If there is no BLAS behind numpy, one can install system wide OpenBLAS by command

$ sudo apt-get install libopenblas-dev liblapack-dev liblapacke-dev

If one don't have administration privileges it is possible to build OpenBLAS (including LAPACK and LAPACKE interfaces) from source (for details see OpenBLAS) and set the environment variable

$ export BLAS_LIB_DIR=path/to/OpenBLAS/lib(64)

to give a hint for the building scripts where to look for the OpenBLAS library.

The Piquasso Boost library can also deployed with MPI support to run large scaled calculations in HPC environments. The python package mpi4py provides the necessary dependencies for the MPI support (which also checks for system wide MPI libraries) In order to enable the MPI support one should define the

$ export PIQUASSOBOOST_MPI=1

environment variable in prior the build. The Piquasso Boost library is supported with AVX/AVX2 and AVX512F kernels. The underlying architecture is determined automatically by building scripts, however the library provides a control switch to compile against AVX512F kernels when it is possible. The AVX152 kernels provide 10-15% speedup at the same CPU clock speed, however, since AVX512 mode usually locks down the CPU clock speed, in overall AVX512F kernels would perform slower than the AVX/AVX2 kernel, if they are not limited by CPU clock speed lock. To check AVX512 capability during compilation and build the code against AVX512F kernels one need to define the

$ export USE_AVX512=1

environment variable. Finally, in order to build Piquasso Boost library including the C test files define the

$ export PIQUASSOBOOST_CTEST=1

environment variable before compiling the library.

Developer build

We recommend installing the Piquasso Boost package in an Anaconda environment for local development. In order to install the necessary requirements, follow the steps below:

Creating new python environment:

$ conda create -n pqboost python=3.10

Activate the new anaconda environment

$ conda activate pqboost

Install dependencies:

$ conda install numpy scipy pip pytest scikit-build tbb-devel tensorflow ninja

$ pip install quantum_blackbird theboss==2.0.3

For running pytest examples one should also install the Strawberry Fields package:

$ pip install strawberryfields

To initialize the correct piquasso package for interfacing with python issue the following commands:

$ git submodule init

$ git submodule update

After the basic environment variables are set and the dependencies are installed, the compilation of the package can be started by the Python command:

$ python3 setup.py build_ext

The command above starts the compilation of the Piquasso Boost C++ library and builds the necessary Python interface extensions in place. After a successful build, one can register the Piquasso Boost package in the Python distribution in developer (i.e. editable) mode by command:

$ python -m pip install -e .

Binary distribution

This section is primarily for maintainers preparing release artifacts. End users should prefer pip install piquassoboost once wheels are published on PyPI.

After the environment variables are set it is possible to build the Piquasso Boost binaries. In order to launch the compilation process from python, scikit-build package is necessary. (It is optional to install the ninja package which speeds up the building process by parallel compilation.) The binary wheel can be constructed by command

$ python3 setup.py bdist_wheel

in the root directory of the Piquasso Boost library. (It is also possible to compile Piquasso Boost package without creating binary wheel with the command python setup.py build_ext) The created Piquasso Boost wheel can be installed on the local machine by issuing the command from the directory path/to/piquasso_boost/library/dist

$ pip3 install piquassoboost-*.whl

We notice, that the created wheel is not portable, since it contains hard coded link to external libraries (TBB and CBLAS).

Source distribution

A portable source distribution of the Piquasso Boost library can be created by a command launched from the root directory of the Piquasso Boost package:

$ python3 setup.py sdist

In order to create a source distribution it is not necessary to set the environment variables, since this script only collects the necessary files and pack them into a tar ball located in the directory path/to/piquasso_boost/library/dist. In order to install the Piquasso Boost package from source tar ball, see the previous section discussing the initialization of the environment variables. The package can be compiled and installed by the command

$ pip3 install piquassoboost-*.tar.gz

issued from directory path/to/piquasso_boost/library/dist (It is optional to install the ninja package which speeds up the building process by parallel compilation.)

Test the Piquasso Boost library

After a succesfull intallation of the Piquasso Boost library one can test its functionalities by calling the tests scripts

$ pytest tests

and

$ pytest -s performance_tests

issued in the root directory of the Piquasso Boost library.