ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran
This library addresses the need for efficient and reproducible Monte Carlo simulations in scientific inference and machine learning, though it appears incremental as it builds on existing Monte Carlo methods with parallelization and automation enhancements.
ParaMonte is a high-performance serial/parallel Monte Carlo simulation library designed to automate and unify sampling of objective functions, particularly for Bayesian models in data science and machine learning, with features like accessibility, scalability, and reproducibility.
ParaMonte (standing for Parallel Monte Carlo) is a serial and MPI/Coarray-parallelized library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference. The ParaMonte library has been developed with the design goal of unifying the **automation**, **accessibility**, **high-performance**, **scalability**, and **reproducibility** of Monte Carlo simulations. The current implementation of the library includes **ParaDRAM**, a **Para**llel **D**elyaed-**R**ejection **A**daptive **M**etropolis Markov Chain Monte Carlo sampler, accessible from a wide range of programming languages including C, C++, Fortran, with a unified Application Programming Interface and simulation environment across all supported programming languages. The ParaMonte library is MIT-licensed and is permanently located and maintained at [https://github.com/cdslaborg/paramonte](https://github.com/cdslaborg/paramonte).