The simulation of topographical manufacturing steps in semiconductor processing, such as deposition and etching, is still a demanding task for today's computers. In addition to the surface evolution, the reaction kinetics on the surface have to be calculated for each time step. While fast techniques for surface propagation have already been developed and successfully applied to topography simulations, as, for example, the sparse field level set method, the efficient determination of the surface velocity, arising from the local etching or deposition rates, is still a problem. Although ballistic transport of particles can be assumed for usual problems, physical models often require the consideration of coverages, flux-dependent sticking probabilities, specular reemission, energy- and direction-dependent sputter/etching yields, etc. Common methods use direct integration to determine particle fluxes on the surface. However, these methods normally use simplified models of surface physics, as, for example, the radiosity method, and scale unfavorably with the size of the simulation domain. For these reasons, we use the Monte Carlo method in combination with fast ray-tracing algorithms to override these difficulties. The local surface velocities are determined by sampling the trajectories and their interactions with the surface of millions of individual particles. Smart ray-tracing techniques, such as spatial subdivision and clustering, are used to efficiently calculate the intersections of particle rays with the surface. The result of all these techniques is an algorithm which scales like O(N*log(N)) with surface discretization and therefore allows large three-dimensional topography simulations. Furthermore, due to the independence of individual particle trajectories in the ballistic transport regime, the Monte Carlo flux calculation can be easily distributed on multi-CPU/core systems. Our approach has already been successfully applied to simulations of reactive ion etching and deposition processes.