An Analytical Model for Performance and Lifetime Estimation of Hybrid DRAM-NVM Main Memories

Abstract

Emerging Non-Volatile Memories (NVMs) have promising advantages (e.g., lower idle power, higher density, and non-volatility) over the existing predominant main memory technology, DRAM. This paper presents an analytical model for hybrid memories based on Markov decision processes. The proposed model estimates the hit ratio and lifetime for various configurations of DRAM-NVM hybrid main memories. Accurate analysis to estimate the effect of data migration policies on the hybrid memory hit ratio, one of the most important factors in hybrid memory performance and lifetime, is also provided by the proposed model. Such an analytical model can aid designers to tune hybrid memory configurations to improve performance and/or lifetime. We present several optimizations that make our model more efficient while maintaining its accuracy. Our experimental evaluations conducted using the PARSEC benchmark suite show that the proposed model (a) accurately predicts the hybrid memory hit ratio compared to the state-of-the-art hybrid memory simulators with an average (maximum) error of 4.61% (13.6%) on a commodity server (equipped with 192GB main memory and quad-core Xeon processor), (b) accurately estimates the NVM lifetime by an average (maximum) error of 2.93% (8.8%), and (c) is on average (up to) 4x (10x) faster than conventional state-of-the-art simulation platforms for hybrid memories.