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The massive addition of cores on a chip is adding more pressure to the accesses to main memory. In order to avoid this bottleneck, we propose the use of a simple producer-consumer model, which allows for the temporary results to be transferred directly from one task to another. These data transfer operations are performed within the chip, using on-chip memory, thus avoiding costly off-chip memory accesses. We implement this model on a real many-core processor, the 48-core Intel Single-chip Cloud Computer processor using its on-chip memory facilities. We find that the Producer-Consumer model adapts to such architectures and allow to achieve good task and data parallelism. For the evaluation of the proposed platform we implement a graph-based application using the Producer- Consumer model. Our tests show that the model scales very well as it takes advantage of the on-chip memory. The execution times of our implementation are up to 9 times faster than the baseline implementation, which relies on storing the temporary results to main memory.