Transformation of synchronous data flow graphs (SDF) into equivalent homogeneous SDF representations has been extensively applied as a pre-processing stage when mapping signal processing algorithms onto parallel platforms. While this transformation helps fully expose task and data parallelism, it also presents several limitations such as an exponential increase in the number of actors and excessive communication overhead. Partial expansion graphs were introduced to address these limitations for multi-core platforms. However, existing solutions are not well-suited to achieve efficient scheduling on many-core architectures. In this article, we develop a new approach that employs cyclo-static data flow techniques to provide a simple but efficient method of coordinating the data production and consumption in the expanded graphs. We demonstrate the advantage of our approach through experiments on real application models.