Doctor of Philosophy

dissertationThe main memory system is a critical component of modern computer systems. Dynamic Random Access Memory (DRAM) based memory designs dominate the industry due to mature device technology and low cost. These designs, however, face several challenges moving forward. These challenges arise due to legacy DRAM device design choices, advances in Central Processing Unit (CPU) design, and the demand for higher memory throughput and capacity from applications. Due to the cost-sensitive nature of the DRAM industry, changes to the device architecture face significant challenges for adoption. There is thus a need to improve memory system designs, ideally without changing the DRAM device architectures. This dissertation addresses the challenges faced by DRAM memory systems by leveraging data management. Historically, data management/placement and its interaction with the memory's hardware characteristics have been abstracted away at the system software level. In this dissertation, we describe mechanisms that leverage data placement at the operating system level to improve memory access latency, power/energy efficiency, and capacity. An important advantage of using these schemes is that they require no changes to the DRAM devices and only minor changes to the memory controller hardware. The majority of the changes are limited to the operating system. This thesis also explores data management mechanisms for future memory systems built using new 3D stacked DRAM devices and point-to-point interconnects. Using the schemes described here, we show improvements in various DRAM metrics. We improve DRAM row-buffer hit rates by co-locating parts of different Operating System (OS) pages in the same row-buffer. This improves performance by 9% and reduces energy consumption by 15%. We also improve page placement to increase opportunities for power-down. This enables a three-fold increase in memory capacity for a given memory power budget. We also show that page placement is an important ingredient in building efficient networks of memories with 3D-stacked memory devices. We report a performance improvement of 49% and an energy reduction of 42% with a design that optimizes page placement and network topology