Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs
Provides a theoretical foundation for parallelizing simulations of a broader class of Markov processes, benefiting computational scientists working on rare event simulations.
The paper generalizes Parallel Replica Dynamics to generic Markov processes, introducing a trajectory fragment framework that enables consistent synchronous and asynchronous algorithms for piecewise deterministic Markov processes.
We study the Parallel Replica Dynamics in a general setting. We introduce a trajectory fragment framework that can be used to design and prove consistency of Parallel Replica algorithms for generic Markov processes. We use our framework to formulate a novel condition that guarantees an asynchronous algorithm is consistent. Exploiting this condition and our trajectory fragment framework, we present new synchronous and asynchronous Parallel Replica algorithms for piecewise deterministic Markov processes.