ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran

ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran

Amir Shahmoradi Fatemeh Bagheri



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Submitted: 29 September 2020 Published: License Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (\IfBeginWith\Url@FormatStringCC BY 4.0).


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 Parallel Delyaed-Rejection Adaptive Metropolis 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

Statement of need

Monte Carlo simulation techniques, in particular, the Markov Chain Monte Carlo (MCMC) are among the most popular methods of quantifying uncertainty in scientific inference problems. Extensive work has been done over the past decades to develop Monte Carlo simulation programming environments that aim to partially or fully automate the problem of uncertainty quantification via Markov Chain Monte Carlo simulations. Example open-source libraries in C/C++/Fortran include \seqsplitMCSim in C (Bois, 2009), \seqsplitMCMCLib and \seqsplitQUESO (Prudencio & Schulz, 2012) libraries in C++, and \seqsplitmcmcf90 in Fortran (Haario, Laine, Mira, & Saksman, 2006). These packages, however, mostly serve the users of one particular programming language environment. Some are able to perform only serial simulations while others are inherently parallelized. Furthermore, majority of the existing packages have significant dependencies on other external libraries. Such dependencies can potentially make the build process of the packages an extremely complex and arduous task due to software version incompatibilities, a phenomenon that has become known as the dependency-hell among software developers.

The ParaMonte library presented in this work aims to address the aforementioned problems by providing a standalone high-performance serial/parallel Monte Carlo simulation environment with the following principal design goals,

  • Full automation of the library’s build process and all Monte Carlo simulations to ensure the highest level of user-friendliness of the library and minimal time investment requirements for building, running, and post-processing of the Monte Carlo and MCMC simulations.

  • Interoperability of the core of the library with as many programming languages as currently possible, including C, C++, Fortran, as well as MATLAB and Python via the \seqsplitParaMonte::MATLAB and \seqsplitParaMonte::Python libraries.

  • High-Performance, meticulously-low-level, implementation of the library that guarantees the fastest-possible Monte Carlo simulations, without compromising the reproducibility of the simulation or the extensive external reporting of the simulation progress and results.

  • Parallelizability of all simulations via both MPI and PGAS/Coarray communication paradigms while requiring zero-parallel-coding efforts from the user.

  • Zero external-library dependencies to ensure hassle-free library builds and Monte Carlo simulation runs.

  • Fully-deterministic reproducibility and automatically-enabled restart functionality for all ParaMonte simulations, up to 16 digits of decimal precision if requested by the user.

  • Comprehensive-reporting and post-processing of each simulation and its results, as well as their efficient compact storage in external files to ensure the simulation results will be comprehensible and reproducible at any time in the distant future.

The Build process

The ParaMonte library is permanently located on GitHub and is available to view at: The build process of the library is fully automated. Extensive detailed instructions are also available on the \IfBeginWith\Url@FormatStringdocumentation website of the library.

For the convenience of the users, each release of the ParaMonte library’s source code also includes prebuilt, ready-to-use, copies of the library for \seqsplitx64 architecture on Windows, Linux, macOS, in all supported programming languages, including C, C++, Fortran. These prebuilt libraries automatically ship with language-specific example codes and build scripts that fully automate the process of building and running the examples.

Where the prebuilt libraries cannot be used, the users can simply call the Bash and Batch build-scripts that are provided in the source code of the library to fully automate the build process of the library. The ParaMonte build scripts are capable of automatically installing any missing components that may be required for the library’s successful build, including the GNU C/C++/Fortran compilers and the \seqsplitcmake build software, as well as the MPI/Coarray parallelism libraries. All of these tasks are performed with the explicit permission granted by the user. The ParaMonte build scripts are heavily inspired by the impressive \seqsplitOpenCoarrays software (Fanfarillo et al., 2014) developed and maintained by the \IfBeginWith\Url@FormatStringSourcery Institute.

The ParaDRAM sampler

The current implementation of the ParaMonte library includes the Parallel Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo (\seqsplitParaDRAM) sampler (Shahmoradi & Bagheri, 2020), (Shahmoradi & Bagheri, 2020a), (Shahmoradi & Bagheri, 2020b), (Kumbhare & Shahmoradi, 2020), and several other samplers whose development is in progress as of writing this manuscript. The ParaDRAM algorithm is a variant of the DRAM algorithm of (Haario et al., 2006) and can be used in serial or parallel mode.

In brief, the ParaDRAM sampler continuously adapts the shape and scale of the proposal distribution throughout the simulation to increase the efficiency of the sampler. This is in contrast to the traditional MCMC samplers where the proposal distribution remains fixed throughout the simulation. The ParaDRAM sampler provides a highly customizable MCMC simulation environment whose complete description goes beyond the scope and limits of this manuscript. All of these simulation specifications are, however, expensively explained and discussed on \IfBeginWith\Url@FormatStringthe documentation website of the ParaMonte library. The description of all of these specifications are also automatically provided in the output \seqsplit*_report.txt files of every simulation performed by the ParaMonte samplers.

Monitoring Convergence

Although the continuous adaptation of the proposal distribution increases the sampling efficiency of the ParaDRAM sampler, it breaks the ergodicity and reversibility conditions of the Markov Chain Monte Carlo methods. Nevertheless, the convergence of the resulting pseudo-Markov chain to the target density function is guaranteed as long as the amount of adaptation of the proposal distribution decreases monotonically throughout the simulation (Shahmoradi & Bagheri, 2020).

Ideally, the diminishing adaptation criterion of the adaptive MCMC methods can be monitored by measuring the total variation distance (TVD) between subsequent adaptively-updated proposal distributions. Except for trivial cases, however, the analytical or numerical computation of TVD is almost always intractable. To circumvent this problem, we have introduced a novel technique in the ParaDRAM algorithm to continuously measure the amount of adaptation of the proposal distribution throughout adaptive MCMC simulations. This is done by computing an upper bound on the value of TVD instead of a direct computation of the TVD.

The mathematics of computing this \seqsplitAdaptationMeasure upper bound is extensively detailed in (Shahmoradi & Bagheri, 2020), (Shahmoradi & Bagheri, 2020a). The computed upper bound is always a real number between 0 and 1, with 0 indicating the identity of two proposal distributions and 1 indicating completely different proposal distributions. The \seqsplitAdaptationMeasure is automatically computed with every proposal adaptation and is reported to the output chain files for all ParaDRAM simulations. It can be subsequently visualized to ensure the diminishing adaptation criterion of the ParaDRAM sampler.

Figure 1: An illustration of the diminishing adaptation of the proposal distribution of the ParaDRAM sampler for an example problem of sampling a 4-dimensional Multivariate Normal distribution. The monotonically-decreasing adaptivity evidenced in this plot guarantees the Markovian property and the asymptotic ergodicity of the resulting Markov chain from the ParaDRAM sampler.

Figure Figure 1 depicts the evolution of the adaptation measure for an example problem of sampling a 4-dimensional MultiVariate Normal Distribution. A non-diminishing evolution of the \seqsplitAdaptationMeasure can be also a strong indicator of the lack of convergence the Markov chain to the target density. The evolution of the covariance matrices of the proposal distribution for the same sampling problem is shown in Figure Figure 2.

Figure 2: A 3-dimensional illustration of the dynamic adaptation of the covariance matrix of the 4-dimensional MultiVariate Normal (MVN) proposal distribution of the ParaDRAM sampler for an example problem of sampling a 4-dimensional MVN distribution.


Two modes of parallelism are currently implemented for all ParaMonte samplers,

  • The Perfect Parallelism (multi-Chain): In this mode, independent instances of the adaptive MCMC sampler run concurrently. Once all simulations are complete, the ParaDRAM sampler compares the output samples from all processors with each other to ensure no evidence for a lack of convergence to the target density exists in any of the output chains.

  • The Fork-Join Parallelism (single-Chain): In this mode, a single processor is responsible for collecting and dispatching information, generated by all processors, to create a single Markov Chain of all visited states with the help of all processors.

For each parallel simulation in the Fork-Join mode, the ParaMonte samplers automatically compute the speedup gained compared to the serial mode. In addition, the speedup for a wide range of the number of processors is also automatically computed and reported in the output \seqsplit*_report.txt files that are automatically generated for all simulations. The processor contributions to the construction of each chain are also reported along with output visited states in the output \seqsplit*_chain.* files. These reports are particularly useful for finding the optimal number of processors for a given problem at hand, by first running a short simulation to predict the optimal number of processors from the sampler’s output information, followed by the production run using the optimal number of processors. For a comprehensive description and algorithmic details see (Shahmoradi & Bagheri, 2020), (Shahmoradi & Bagheri, 2020a).

Figure 3: An illustration of the contributions of 512 Intel Xeon Phi 7250 processors to a ParaMonte-ParaDRAM simulation parallelized via the Fork-Join paradigm. The predicted best-fit Geometric distribution from the post-processing phase of the ParaDRAM simulation is shown by the black line. The data used in this figure is automatically generated for each parallel simulation performed via any of the ParaMonte samplers.

As we argue in (Shahmoradi & Bagheri, 2020, Shahmoradi & Bagheri (2020a)), the contribution of the processors to the construction of a Markov Chain in the Fork-Join parallelism paradigm follows a Geometric distribution. Figure Figure 3 depicts the processor contributions to an example ParaDRAM simulation of a variant of Himmelblau’s function and the Geometric fit to the distribution of processor contributions. Figure Figure 4 illustrates an example predicted strong-scaling behavior of the sampler and the predicted speedup by the sampler for a range of processor counts.

Figure 4: A comparison of the actual strong scaling behavior of an example ParaMonte-ParaDRAM simulation from 1 to 1088 processors with the strong-scaling behavior predicted during the post-processing phases of ParaDRAM simulations. The data used in this figure is automatically generated for each parallel simulation performed via any of the ParaMonte samplers.

Efficient compact storage of the output chain

Efficient continuous external storage of the output of ParaDRAM simulations is essential for both the post-processing of the results and the restart functionality of the simulations, should any interruptions happen at runtime. However, as the number of dimensions or the complexity of the target density increases, such external storage of the output can easily become a challenge and a bottleneck in the speed of an otherwise high-performance ParaDRAM sampler. Given the currently-available computational technologies, input/ouput (IO) to external hard-drives can be 2-3 orders of magnitude slower than the Random Access Memory (RAM) storage.

To alleviate the effects of such external-IO speed bottlenecks, the ParaDRAM sampler implements a novel method of carefully storing the resulting MCMC chains in a small compact, yet ASCII human-readable, format in external output files. This compact-chain (as opposed to the verbose (Markov)-chain) format leads to significant speedup of the simulation while requiring 4-100 times less external memory to store the chains in the external output files. The exact amount of reduction in the external memory usage depends on the efficiency of the sampler. Additionally, the format of output file can be set by the user to \seqsplitbinary, further reducing the memory foot-print of the simulation while increasing the simulation speed. The implementation details of this compact-chain format are extensively discussed in (Shahmoradi & Bagheri, 2020), (Shahmoradi & Bagheri, 2020a).

Sample refinement

In addition to the output \seqsplit*_progress.txt, \seqsplit*_report.txt, \seqsplit*_chain.txt files, each ParaDRAM sampling generates a \seqsplit*_sample.txt file containing the final refined decorrelated sample from the objective function. To do so, the sampler computes the Integrated AutoCorrelation (IAC) of the chain along each dimension of the domain of the objective function. However, the majority of existing methods for the calculation of IAC tend to underestimate this quantity.

Therefore, to ensure the final sample resulting from a ParaDRAM simulation is fully decorrelated, we have implemented a novel approach that aggressively and recursively refines the resulting Markov chain from a ParaDRAM simulation until no trace of autocorrelation is left in the final refined sample. This approach optionally involves two separate phases of sample refinement,

  1. 1.

    At the first stage, the Markov chain is decorrelated recursively, for as long as needed, based on the IAC of its compact format, where only the the uniquely-visited states are kept in the (compact) chain.

  2. 2.

    Once the Markov chain is refined such that its compact format is fully decorrelated, the second phase of decorrelation begins, during which the Markov chain is decorrelated based on the IAC of the chain in its verbose (Markov) format. This process is repeated recursively for as long as there is any residual autocorrelation in the refined sample.

We have empirically noticed, via numerous experimentations, that this recursive aggressive approach is superior to other existing methods of sample refinement in generating final refined samples that are neither too small in size nor autocorrelated.

The restart functionality

Each ParaMonte sampler is automatically capable of restarting an existing interrupted simulation, whether in serial or parallel. All that is required is to rerun the interrupted simulation with the same output file names. The ParaMonte samplers automatically detect the presence of an incomplete simulation in the output files and restart the simulation from where it was left off. Furthermore, if the user sets the seed of the random number generator of sampler prior to running the simulation, the ParaMonte samplers are capable of regenerating the same chain that would have been produced if the simulation had not been interrupted in the first place. Such fully-deterministic reproducibility into-the-future is guaranteed with 16 digits of decimal precision for the results of any ParaMonte simulation. To our knowledge, this is a unique feature of the ParaMonte library that does not appear to exist in any of the contemporary libraries for Markov Chain Monte Carlo simulations.

Documentation and Repository

The ParaMonte library was originally developed in 2012 and remained in private use for the research needs of the developers of the library (Shahmoradi, 2013), (Shahmoradi, 2013), (Shahmoradi & Nemiroff, 2014), (Shahmoradi & Nemiroff, 2015), (Shahmoradi & Nemiroff, 2019), (Osborne, Shahmoradi, & Nemiroff, 2020), (Osborne, Shahmoradi, & Nemiroff, 2020). The library was further developed in 2018 and made available to the public with its first official release in 2020.

Extensive documentation and examples in C, C++, Fortran (as well as other programming languages) are available on the documentation website of the library at: The ParaMonte library is MIT-licensed and is permanently located and maintained at


We thank the Texas Advanced Computing Center for providing the supercomputer time for testing and development of this library.


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