We investigate the problem of learning a topic model - the well-known Latent
Dirichlet Allocation - in a distributed manner, using a cluster of C processors
and dividing the corpus to be learned equally among them. We propose a simple
approximated method that can be tuned, trading speed for accuracy according to
the task at hand. Our approach is asynchronous, and therefore suitable for
clusters of heterogenous machines.