As core counts expand through chiplet integration, advanced packaging, and memory expansion technologies, modern systems increasingly rely on large-scale and coherent shared-memory architectures. While coherence protocols are well studied, the empirical behavior of coherent data movement at system scale remains poorly understood. This work presents a detailed characterization of coherent data movement using cyclelevel full-system simulation of a modeled large-scale hierarchical shared-memory system. Across representative HPC and graph processing workloads, we find that coherence overheads are dominated by a small subset of high-impact pages, despite most pages exhibiting limited sharing. These pages combine wide socket span, high access frequency, and millisecond-scale temporal persistence, generating sustained long-distance coherence traffic that frequently propagates beyond the local chassis into higher levels of the interconnect. Together, these observations indicate that coherence cost and challenges are driven by a sparse but dominant tail of shared data, motivating architectural mechanisms that adapt data placement and coherence anchoring to page-level access behavior to make coherence mechanisms more efficient.

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demands. In this paper, we propose TDRAM, a novel DRAM microarchitecture tailored for caching. TDRAM enhances existing DRAM, such as HBM3, by adding small, low-latency mats to store tags and metadata on the same die as the data mats. These mats enable tag and data access in lockstep, in-DRAM tag comparison, and conditional data response based on the comparison result (reducing wasted data transfers), akin to SRAM cache mechanisms. TDRAM further optimizes hit and miss latencies through opportunistic early tag probing. Moreover, TDRAM introduces a flush buffer to store conflicting dirty data on write misses, eliminating data bus turnaround delays on write demands. We evaluate TDRAM in a full-system simulation using a set of HPC workloads with large memory footprints, showing that TDRAM, on average, provides $2.65 \times$ faster tag checks, $1.23 \times$ speedup, and 21% less energy consumption compared to state-of-the-art commercial and research designs.