The biomedical imagery, the numeric communications, the acoustic signal processing and many others digital signal processing (DSP) applications are present more and more in the numeric world. They process growing data volume which is represented with more and more accuracy, and use complex algorithms with time constraints to satisfying. Consequently, a high requirement of computing power characterize them. To satisfy this need, it's inevitable today to use parallel and heterogeneous

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... s in order to speedup the processing, where the best examples are today's supercomputers like "Tianhe-2" and "Titan" of Top500 ranking. These architectures with their multi-core nodes supported by many-core accelerators offer a good response to this problem. However, they are still hard to program to make performance because of many reasons: Parallelism expression, task synchronization, memory management, hardware specifications handling, load balancing . . . In the present work, we are characterizing DSP applications and propose a programming model based on their distinctiveness in order to implement them easily and efficiently on heterogeneous clusters.