Processing-in-memory (PIM) alleviates data-movement bottlenecks by placing computation near or inside memory. Two general-purpose PIM paradigms are now practical: processing-near-memory (PNM) and processing-using-memory (PUM). Although both target regular, data-intensive, data-parallel workloads, they remain difficult to program because data organization is tightly coupled to computation and differs across substrates. Thus, programmers must manually manage substrate-specific data partitioning, movement, layout, and tiling, preventing program portability across PIM systems. We propose DaPPA (data-parallel processing-in-memory architecture), a programming framework that exposes a single high-level data-parallel pattern interface and retargets it to multiple PIM substrates. The key idea of DaPPA is to decouple the expression of data parallelism from substrate-specific data organization: programmers express computation once using data-parallel patterns and a dataflow interface, while specialized backends automatically generate data orchestration for PNM and multi-level tiling for PUM. We evaluate DaPPA on representative substrates from both PIM paradigms. On the UPMEM PNM system, DaPPA outperforms hand-tuned implementations by 1.6x on average with 95% fewer lines-of-code, and outperforms the state-of-the-art SimplePIM framework by 1.4x on average with 62% fewer lines-of-code. On a SIMDRAM-like PUM substrate, DaPPA outperforms the state-of-the-art MIMDRAM compiler by 2.0x on average (up to 6.4x) across 12 workloads. DaPPA can deploy all ten of our PNM workloads, whereas CINM, the closest framework targeting both paradigms, can deploy only three, on which DaPPA is 2.7--6.8x faster. We open-source DaPPA at https://github.com/CMU-SAFARI/DaPPA.

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