Machine Learning Augmented Hybrid Memory Management

Abstract

The integration of emerging non volatile memory hardware technologies into the main memory substrate, enables massive memory capacities at a reasonable cost in return for slower access speeds. This heterogeneity, along with the greater irregularity in the behavior of emerging workloads, render existing memory management approaches ineffective. This creates a significant gap between the realized vs. achievable performance and efficiency. At the same time, resource management solutions augmented with machine learning show great promise for fine-tuning system configuration knobs and predicting future behaviors. This thesis builds novel system-level mechanisms and reveals new insights towards the practical integration of machine learning in hybrid memory management. The specific contributions of this thesis is a machine learning augmented memory manager, coupled with insightful mechanisms to reduce the associated learning overheads and fine-tune critical operational parameters. The impact of this thesis is realizing an average of 3x application performance improvements and setting the new state-of-the-art in hybrid memory management.