PMMC - Python bindings for Mesh-based Monte Carlo (MMC) photon transport simulator

• Copyright: (C) Qianqian Fang (2025-2026) <q.fang at neu.edu>
• License: GNU Public License V3 or later
• Version: 0.6.0
• URL: https://pypi.org/project/pmmc/
• Github: https://github.com/fangq/mmc

This module provides a Python binding for Mesh-based Monte Carlo (MMC). For other binaries, including the standalone executable and the MATLAB bindings, see our website.

Mesh-based Monte Carlo (MMC) is a 3D Monte Carlo (MC) simulation software for photon transport in complex turbid media. MMC combines the strengths of the MC-based technique and the finite-element (FE) method: on the one hand, it can handle general media, including low-scattering ones, as in the MC method; on the other hand, it can use an FE-like tetrahedral mesh to represent curved boundaries and complex structures, making it even more accurate, flexible, and memory efficient. MMC uses the state-of-the-art ray-tracing techniques to simulate photon propagation in a mesh space. It has been extensively optimized for excellent computational efficiency and portability.

How to Install

• PIP: pip install pmmc, see https://pypi.org/project/pmmc/

Binary wheels are available for Linux (x86_64), macOS (Apple silicon and Intel) and Windows. They include the multi-threaded SSE (CPU) and the OpenCL (CPU/GPU) backends.

Runtime Dependencies

• CPU or GPU: An OpenCL-capable CPU or GPU; most modern CPUs or GPUs support OpenCL - an industrial-standard heterogeneous computing library and specification (https://www.khronos.org/opencl/)
• OpenCL CPU or GPU runtime/driver: Both NVIDIA and AMD GPU graphics drivers should contain out-of-box OpenCL runtimes or drivers; for Intel GPUs, one should install additional OpenCL runtime support from https://github.com/intel/compute-runtime or install the intel-opencl-icd package if the OS provides (such as Ubuntu 22.04); one can also install an open-source OpenCL runtime POCL, using package manager such as sudo apt-get install pocl-opencl-icd. However, POCL's support is largely limited to CPUs. You do not need to install CUDA SDK to use pmmc.
• Python: Python 3.6 and newer is required. Python 2 is not supported.
• numpy: Used to pass/receive volumetric information to/from pmmc. To install, use either conda or pip package managers: pip install numpy or conda install numpy
• iso2mesh is an easy-to-use mesh generator for creating the tetrahedral meshed domain for pmmc, install it with pip install iso2mesh
• (optional) jdata: Only needed to read/write JNIfTI output files. To install, use pip: pip install jdata on all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter using apt-get: sudo apt-get install python3-jdata. See https://pypi.org/project/jdata/ for more details.
• (optional) bjdata: Only needed to read/write BJData/UBJSON files. To install, run pip install bjdata on all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter using apt-get: sudo apt-get install python3-bjdata. See https://pypi.org/project/bjdata/ for more details.
• (optional) matplotlib: For plotting the results. To install, run either pip install matplotlib or conda install matplotlib

Build Instructions

Build Dependencies

• Operating System: pmmc and mmc can be compiled on most OSes, including Windows, Linux and MacOS.

• OpenCL library: compiling mmc or pmmc requires to link with libOpenCL.so on Linux, or libOpenCL.dylib on MacOS or OpenCL.dll on Windows. These libraries should have been installed by either graphics driver or OpenCL runtimes.

• Python Interpreter: Python 3.6 or above. The pip Python package manager and the wheel package (available via pip) are not required but recommended.

• C/C++ Compiler: pmmc can be compiled using a wide variety of C compilers, including

  • GNU GCC for Linux, MacOS (installed via MacPorts or brew), and Windows (installed via msys2, mingw64 or cygwin64)
  • Microsoft Visual Studio C/C++ Compiler for Windows.
  • Apple Clang for macOS, available via Xcode.

  Refer to each OS's online documentations for more in-depth information on how to install these compilers. MacOS provides built-in OpenCL library support.

• OpenMP: The installed C/C++ Compiler should have support for OpenMP. GCC and Microsoft Visual Studio compiler support OpenMP out of the box. Apple Clang, however, requires manual installation of OpenMP libraries for Apple Clang. The easiest way to do this is via the Brew package manager, preferably after selecting the correct Xcode version:

brew install libomp
brew link --force libomp

• CMake: CMake version 3.15 and later is required. Refer to the CMake website for more information on how to download. CMake is also widely available on package managers across all operating systems.

Build Steps

1. Ensure that cmake, python and the C/C++ compiler are all located over your PATH. This can be queried via echo $env:PATH on Windows or echo $PATH on Linux. If not, locate them and add their folder to the PATH.

2. Clone the repository and switch to the pmmc/ folder:

git clone --recursive https://github.com/fangq/mmc.git
cd mmc/pmmc

3. One can run python3 setup.py install or python3 -m pip install . to both locally build and install the module

4. If one only wants to locally build the module, one should run python3 -m pip wheel .

5. If the binary module is successfully built locally, you should see a binary wheel file pmmc-X.X.X-cpXX-cpXX-*.whl stored inside the mmc/pmmc folder. You can install this wheel package using python3 -m pip install --force-reinstall pmmc-*.whl to force installing this locally compiled pmmc module and overwrite any previously installed versions.

6. (optional) To also build the CUDA backend (cfg["compute"] = "cuda", NVIDIA GPUs only), install the CUDA toolkit and pass -DBUILD_CUDA=ON to CMake, for example CMAKE_ARGS="-DBUILD_CUDA=ON" python3 -m pip install .

How to use

The PMMC module is easy to use. You can use the pmmc.gpuinfo() function to first list the OpenCL CPUs and GPUs available on your computer, then call the run() function to launch a photon simulation. The compute backend is selected by cfg['compute']: 'opencl' (default), 'sse' (multi-threaded CPU, cfg['gpuid']=-1), or 'cuda' if pmmc was built with CUDA; 'optix' is accepted as in mmclab and runs with OpenCL when OptiX is not built in.

A simulation can be defined conveniently in two approaches - a one-liner and a two-liner:

• For the one-liner, one simply passes each MMC simulation setting as a keyword argument. The supported setting names are compatible with nearly all the input fields of the MATLAB version of MMC - MMCLAB

import pmmc
import numpy as np
import matplotlib.pyplot as plt

import iso2mesh as i2m
node, face, elem = i2m.meshabox([0, 0, 0], [60, 60, 60], 10, 100)  # create a mesh

gpus = pmmc.gpuinfo()  # list all available GPUs

res = pmmc.run(nphoton=1000000, node=node, elem=elem, elemprop=np.ones(elem.shape[0]), tstart=0, tend=5e-9,
               tstep=5e-9, srcpos=[30,30,0], srcdir=[0,0,1], prop=np.array([[0, 0, 1, 1], [0.005, 1, 0.01, 1.37]]))
res['flux'].shape

plt.imshow(np.log10(res['flux'][30,:, :].squeeze()))
plt.show()

• Alternatively, one can also define a Python dict object containing each setting as a key, and pass on the dict object to pmmc.run()

import pmmc
import numpy as np
cfg = {'nphoton': 1000000, 'node': node, 'elem': elem, 'elemprop': np.ones(elem.shape[0]), 'tstart':0, 'tend':5e-9, 'tstep':5e-9,
       'srcpos': [30,30,0], 'srcdir':[0,0,1], 'prop':[[0,0,1,1],[0.005,1,0.01,1.37]]}
res = pmmc.run(cfg)

pmmc.run() calls the binary module only. pmmc.mmclab(cfg) additionally applies the same pre- and post-processing as mmclab.m (for example, reorienting the elements, finding the initial element of the source, and unpacking the detected photon and trajectory data into dicts), and is recommended when porting MMCLAB scripts. Python ports of all the MMCLAB example scripts can be found in the example/ folder (see example/README.md).

Log output

The messages of pmmc.run() - the echo of the cfg fields and the simulation log (the banner, timings and absorbed fraction) - are controlled by cfg['flog'], as in MATLAB: 2 (default) prints them to Python's sys.stdout, so they show in Jupyter notebooks; 1 prints them to the stdout of the process, and 0 does the same without the banner; a string appends them to that file. cfg['flog'] = os.devnull silences everything; errors are still raised as Python exceptions.

Reproducible simulations

Photons are scheduled to the CPU threads and GPU threads dynamically for speed, so two runs with the same cfg['seed'] agree statistically but do not simulate the identical set of photons. To reproduce a simulation exactly on the CPU, use the SSE backend with a single OpenMP thread (set the environment variable OMP_NUM_THREADS=1 before starting Python).