Q‐selector‐based prefetching method for dram/nvm hybrid main memory system

This research is to design a Q‐selector‐based prefetching method for a dynamic random-access memory (DRAM)/ Phase‐change memory (PCM)hybrid main memory system for memory-intensive big data applications generating irregular memory accessing streams. Specifically, the proposed method fully exploits the advantages of two‐level hybrid memory systems, constructed as DRAM devices and non‐volatile memory (NVM) devices. The Q‐selector‐based prefetching method is based on the Q‐learning method, one of the reinforcement learning algorithms, which determines a near‐optimal prefetcher for an application’s current running phase. For this, our model analyzes real‐time performance status to set the criteria for the Q‐learning method. We evaluate the Q‐selector‐based prefetching method with workloads from data mining and data‐intensive benchmark applications, PARSEC‐3.0 and graphBIG. Our evaluation results show that the system achieves approximately 31% performance improvement and increases the hit ratio of the DRAM-cache layer by 46% on average compared to a PCM‐only main memory system. In addition, it achieves better performance results compared to the state‐of‐the‐art prefetcher, access map pattern matching (AMPM) prefetcher, by 14.3% reduction of execution time and 12.89% of better CPI enhancement.