This paper presents an approach for reducing the memory requirements of dataflow applications, while minimizing the execution period when deployed on a many-core target. Often, straightforward implementations of dataflow applications suffer from data duplication if identical data has to be processed by multiple actors. In fact, multi-cast actors can produce huge memory overheads when storing and communicating copies of the same data. As a remedy, so-called Multi-Reader Buffers (MRBs) can be utilized to forward identical data to multiple actors in a FIFO manner while storing each data item only once. However, MRBs may increase the achievable period due to communication contention when accessing the shared data. A novel multi-objective design space exploration approach is proposed that selectively replaces multi-cast actors with MRBs and explores actor and FIFO channel mappings to find trade-offs between the objectives of period, memory footprint, and core cost. Our approach considers (i) memory-size constraints, (ii) hierarchical memories to implement the buffers, (iii) supports heterogeneous many-core platforms, and (iv) optimizes the buffer placement and overall scheduling to minimize the execution period by proposing a novel combined actor and communications scheduling heuristic for period minimization called CAPS-HMS. Our results show that the explored Pareto fronts improve a hypervolume indicator over a reference approach by up to 66 % for small to mid-size applications and 90 % for large applications. Moreover, selectively replacing multi-cast actors with corresponding MRBs proves to be always superior to never or always replacing them. Finally, it is shown that the quality of the explored Pareto fronts does not degrade when replacing the efficient scheduling heuristic CAPS-HMS by an exact integer linear programming (ILP) solver.