The dataflow paradigm frees the designer to focus on the functionality of an application, independently from the underlying architecture executing it. While mapping the dataflow computational part to the cores seems obvious, the memory aspects do not match accordingly. Dataflow compilers usually do not consider the presence of caches when generating code. A generally accepted idea is that bigger and multi-level caches improve the performance of applications. Unfortunately, state-of-the-art dataflow compilers may prove the exception to this rule. This paper presents two efficient memory management strategies for dataflow applications through a study on the impact of sharing, size, and the number of levels of caches on them. The results show that bigger is not always better, and the foreseen future of more cores and bigger caches do not guarantee software-free better performance for dataflow applications. We propose two strategies, that can be used concurrently, to address the memory aspects of the dataflow model: copy-onwrite and non-temporal memory transfers. Experimental results show that we speed up a computer stereo vision application by 2.1× and reduce the number of L1 data cache misses by 45% while maintaining the actors' source code and design intact.