Mapping a dataflow application onto a heterogeneous multiprocessor platform cannot longer be static. It has to adapt dynamically depending on the data and on the communication between the computation cores. This is typically the case for mobile devices that run multimedia applications. This paper presents an algorithm fast enough to be executed at run-time. In addition to computation cost, our approach relies on a communication model to estimate the delay for transmitting data. The algorithm is compared with METIS tool for random dataflow graphs and two video decoders, MPEG4-SP and HEVC, considering heterogeneous multiprocessor platforms composed of 4 to 8 processors and 6 accelerators. Results on a Zynq platform show that our algorithm is about 40x faster than METIS tool for the same throughput (frames per second) on a platform with 8 processors and 6 accelerators.