The emergence of hardware accelerators, such as graphics processing units (GPUs), has challenged the interaction between processing elements (PEs) and main memory. In architectures like the Cell/B.E. or GPUs, the PEs incorporate local memories which are fed with data transferred from memory using direct memory accesses (DMAs). We expect that chip multiprocessors (CMP) with DMA-managed local memories will become more popular in the near future due to the increasing interest in accelerators. In this work we show that, in that case, the way cache hierarchies are conceived should be revised. Particularly for last-level caches, the norm today is to use latency-aware organizations. For instance, in dynamic nonuniform cache architectures (D-NUCA) data is migrated closer to the requester processor to optimize latency. However, in DMA-based scenarios, the memory system latency becomes irrelevant compared with the time consumed for moving the DMA data, so latency-aware designs are, a priori, inefficient. In this work, we revisit the last-level cache designs in DMA-based CMP architectures with master-worker execution. Two scenarios are evaluated. First, we consider a set of private caches with data replication across them, where coherency of the copies is ensured through a hardware protocol. In this scenario, a PE has a nearby copy of the datum, improving cache access latency. Second, we consider a partitioned cache, where the allocation of a datum to a cache block is determined based on its physical address. In this scenario, there are no copies of data, and access to a datum has a variable latency. In contrast with traditional load/store-based architectures, we found that the partitioned last-level cache scheme outperforms the cache with data replication for DMA-based scenarios.