Parallelization of adaptive MC integrators

Parallelization of adaptive MC Integrators

Monte Carlo (MC) methods for numerical integration seem to be embarassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS-Algorithm as an example an economic method of semi-micro parallelization w...

The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning

Since their impressive successes in various areas of large-scale parallelization, recent techniques like UCT and other Monte-Carlo planning variants (Kocsis and Szepesvari, 2006a) have been extensively studied (Coquelin and Munos, 2007; Wang and Gelly, 2007). We here propose and compare various forms of parallelization of bandit-based tree-search, in particular for our computer-go algorithm XYZ.

Adaptive Variational Integrators

It is now well known that symplectic integrators lose many of their desirable properties when variable step sizes are used. The most common approach to combine adaptive step sizes and symplectic integrators involves the Poincaré transformation of the original Hamiltonian. In this article, we provide a framework for the construction of variational integrators using the Poincaré transformation. S...

Adaptive Parallelization of Model-Based Head Tracking

Parallelization of an Adaptive Vlasov Solver

This paper presents an efficient parallel implementation of a Vlasov solver. Our implementation is based on an adaptive numerical scheme of resolution. The underlying numerical method uses a dyadic mesh which is particularly well suited to manage data locality. We have developed an adapted data distribution pattern based on a division of the computational domain into regions and integrated a lo...