distributedMonte

distributedMonte is a software framework designed to enable scalable Monte Carlo simulations across distributed computing environments. It provides tools for parallel execution, reproducible random number generation, and integration with high-performance clusters and cloud infrastructure.

Key features include a distributed task scheduler with load balancing across compute nodes, and support for multiple Monte Carlo methods such as standard Monte Carlo, Markov chain Monte Carlo, importance sampling, and sequential Monte Carlo. The framework emphasizes reproducibility through carefully managed random number streams and seed control, along with fault tolerance through checkpointing and state persistence. It offers APIs in multiple languages, notably Python and C++, and can be used via a command-line interface for setup and execution.

Architecturally, distributedMonte typically comprises a client library, a set of worker processes on compute nodes, and a central scheduler or manager that coordinates task assignment and result collection. Communication may rely on MPI, gRPC, or HTTP-based protocols, and the system is designed to work with diverse execution environments, including Kubernetes clusters, Slurm-based HPC systems, and cloud runtimes. Data management capabilities support streaming results, in-memory aggregation, and online diagnostics to monitor convergence and performance during runs.

Common use cases span uncertainty quantification, quantitative finance, physics and engineering simulations, Bayesian inference, and epidemiological modeling. The framework is intended to simplify scaling of Monte Carlo workloads while maintaining reproducibility and accuracy, although performance depends on factors such as network latency, RNG quality, and the nature of the underlying problem.

See also: Monte Carlo methods; distributed computing; MPI; Ray; Dask.