GPUs have largely entered HPC clusters, as shown by the top entries of the latest top500 issue. Exploiting such machines is however very challenging, not only because of combining two separate paradigms, MPI and CUDA or OpenCL, but also because nodes are heterogeneous and thus require careful load balancing within nodes themselves. The current paradigms are usually limited to only offloading parts of the computation and leaving CPUs idle, or they require static work partitioning between CPUs and GPUs. To handle single-node architecture heterogeneity, we have previously proposed StarPU, a runtime system capable of dynamically scheduling tasks in an optimized way on such machines. We show here how the task paradigm of StarPU has been combined with MPI communications, and how we extended the task paradigm itself to allow mapping the task graph on MPI clusters such as to automatically achieve an optimized distributed execution. We show how a sequential-like Cholesky source code can be easily extended into a scalable distributed parallel execution, and already exhibits a speedup of 5 on 6 nodes.