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<a href="#paramonte-plain-powerful-parallel-monte-carlo-library">Overview</a> |
<a href="#installation">Installation</a> |
<a href="#dependencies">Dependencies</a> |
<a href="#parallelism">Parallelism</a> |
<a href="#example-usage-instructions">Examples</a> |
<a href="#citing-paramonte">Acknowledgments</a> |
<a href="#license">License</a> |
<a href="#authors-and-contributors">Authors</a>
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ParaMonte: Parallel Monte Carlo and Machine Learning
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical density functions of arbitrary dimensions and
Machine Learning (ML) algorithms for scientific inference, with the design goal of unifying automation (of simulations and tasks),
user-friendliness (of algorithms), accessibility (from any platform or programming environment),
high-performance (at runtime), and scalability (across many parallel processors).
For more information on the installation, usage, and examples, visit https://www.cdslab.org/paramonte
ParaMonte Design Goals
ParaMonte has been developed while bearing the following design goals in mind:
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Full automation of Monte Carlo and Machine Learning simulations as much as possible to ensure user-friendliness
of the library and minimal time investment requirements for building, running, and post-processing simulation models.
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Interoperability of the core library with as many programming languages as currently possible,
including C, C++, Fortran, MATLAB, and Python, with ongoing efforts to support other popular programming languages.
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High-Performance meticulously-low-level library implementation to ensure the fastest-possible inferences.
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Parallelizability of all simulations via shared-memory OpenMP threads and two-sided and one-sided
MPI/Coarray distributed communications while requiring zero-parallel-coding efforts by the user.
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Zero-dependence on external libraries to ensure hassle-free ParaMonte library builds and ParaMonte simulation runs.
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Fully-deterministic reproducibility and automatically-enabled restart functionality
for all simulations with arbitrary digits of precision as requested by the user.
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Comprehensive reporting and post-processing of simulations and ML tasks, as well as their automatic storage in
external files to ensure the simulation results will be understandable and reproducible at any time in the foreseeable future.
Quick Start
- Follow the quick start instructions in this QUICKSTART.md file.
- For programming languages other than C, C++, Fortran, see the quick start section of the README.md file in the corresponding
language's source subfolder in the src folder in the root directory of the ParaMonte repository.
Installation
The prebuilt, ready-to-use libraries are available on
the release page of the ParaMonte library on GitHub.
Each prebuilt ParaMonte library automatically ships with a full-fledged set of example codes and build scripts.
Alternatively, you can build the library from the source
in the project's GitHub repository.
The ParaMonte library installation/build process is fully automated for all supported programming languages.
Currently, the following compiler suites are supported for builds from source,
| Compiler Suite | Windows (amd64) | Linux (amd64) | macOS (amd64) | macOS (arm64) |
|----------------------------------:|:---------------:|:-------------:|:-------------:|:-------------:|
| GNU Compiler Collection >= 10.3 | ✅ | ✅ | ✅ | ✅ |
| Intel OneAPI >= 2021.8.0 | ✅ | ✅ | ✅ | ❌ |
with the following essential build systems,
| Dependency | Windows (amd64) | Linux (amd64) | macOS (amd64) | macOS (arm64) |
|----------------------------------:|:---------------:|:-------------:|:-------------:|:-------------:|
| CMake >= 3.21 | ✅ | ✅ | ✅ | ✅ |
Dependencies
Aside from an optional MPI runtime library for MPI-parallel simulations,
the ParaMonte library core has zero dependency on external third-party libraries or packages.
Parallelism
The ParaMonte library relies on three independent parallelism paradigms for parallel applications:
- The Coarray parallelism (only available for select rout
