This paper studies the benefits of compiling data-parallel languages onto a multithreaded runtime environment providing dynamic thread migration facility. Each abstract process is mapped onto a thread, so that dynamic load balancing can be achieved by migrating threads among the processing nodes. We describe and evaluate an implementation of this idea in the Adaptor HPF and the UNH C* data-parallel compilers. We show that no deep modifications of the compilers are needed, and that the overhead of managing threads can be kept small. As an experimental validation, we report on an HPF implementation of the Gauss Partial Pivoting algorithm. We show that the initial BLOCK data distribution with our dynamic load balancing scheme can reach the performance of the optimal CYCLIC distribution.