Global Clean Page First Replacement and Index Aware Multi-Stream Prefetcher in Hybrid Memory Architecture

Ing-Chao Lin, Da-Wei Chang, Wei Jun Chen, Jian Ting Ke, Po Han Huang

Research output: Contribution to journalArticle


As cloud computing and big data applications become more popular, the demand for large capacity memory and data preservation in memory increases. Therefore, non-volatile memory (NVM) with high capacity is being actively developed. A hybrid memory that comprises both NVM and DRAM and provides both high access speed and non-volatility has become a major trend. However, compared to DRAM, NVM in the hybrid memory typically suffers from a shorter lifetime and higher latency. To improve the lifetime and address the latency issues associated with hybrid memory, we propose a Global Clean Page First replacement (GCPF) to reduce the write operations to NVM. We also propose an index-aware multi-stream prefetcher (IAMSP) that considers the indexes of prefetch candidates individually so as to prefetch pages from NVM more accurately. Benchmarks with a large memory footprint are used to evaluate the proposed schemes. The experimental results show that GCPF enhances lifetime by 56.8% as compared to LRU, on average. When applying prefetching schemes on GCPF, the lifetime is insignificantly degraded. In addition, IAMSP reduces DRAM misses by 42.0% as compared to LRU, while a modern prefetcher that can change the prefetch degree dynamically only reduces DRAM misses by 38.0%, on average. When applying both GCPF and IAMSP, the average access latency can be reduced by 28.8% as compared to LRU.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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