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As memory capacity has outstripped TLB coverage, large data applications suffer from frequent page table walks. We investigate two complementary techniques for addressing this cost: reducing the number of accesses required and reducing the latency of each access. The first approach is accomplished by opportunistically "flattening" the page table: merging two levels of traditional 4KB page table nodes into a single 2MB node, thereby reducing the table's depth and the number of indirections required to search it. The second is accomplished by biasing the cache replacement algorithm to keep page table entries during periods of high TLB miss rates, as these periods also see high data miss rates and are therefore more likely to benefit from having the smaller page table in the cache than to suffer from increased data cache misses. We evaluate these approaches for both native and virtualized systems and across a range of realistic memory fragmentation scenarios, describe the limited changes needed in our kernel implementation and hardware design, identify and address challenges related to self-referencing page tables and kernel memory allocation, and compare results across server and mobile systems using both academic and industrial simulators for robustness. We find that flattening does reduce the number of accesses required on a page walk (to 1.0), but its performance impact (+2.3%) is small due to Page Walker Caches (already 1.5 accesses). Prioritizing caching has a larger effect (+6.8%), and the combination improves performance by +9.2%. Flattening is more effective on virtualized systems (4.4 to 2.8 accesses, +7.1% performance), due to 2D page walks. By combining the two techniques we demonstrate a state-of-the-art +14.0% performance gain and -8.7% dynamic cache energy and -4.7% dynamic DRAM energy for virtualized execution with very simple hardware and software changes.