The Fast Multipole Method (FMM) is considered as one of the top ten algorithms of the 20th century. The FMM can speed up solving of electromagnetic scattering problems. With N being the number of unknowns, the complexity usually O(N 2) becomes O(N log N ) allowing a problem with hundreds of millions of complex unknowns to be solved. The FMM applied in our context has a serious drawback: the parallel version is not very scalable. In this paper, we present a new approach in order to overcome this limit. We use StarPU, a runtime system for heterogeneous multicore architectures. Thus, our aim is to have good efficiency on a cluster with hundreds of CPUs, and GPUs. Much work have been done on parallelization with advanced distribution techniques but never with such a runtime system. StarPU is very useful, especially for the multi-level algorithm on a hybrid machine. At present, we have developed a multi-core and a GPU version. The techniques for distributing and grouping the data are detailed in this paper. The first results of the strategy used are promising.