On Automated Feedback-Driven Data Placement in Multi-tiered Memory

Recent emergence of systems with multiple performance and capacity tiers of memory invites a fresh consideration of strategies for optimal placement of data into the various tiers. This work explores a variety of cross-layer strategies for managing application data in multitiered memory. We propose new profiling techniques based on the automatic classification of program allocation sites, with the goal of using those classifications to guide memory tier assignments.We evaluate our approach with different profiling inputs and application strategies, and show that it outperforms other state-of-the-art management techniques.