近年來，由於大數據應用需要大量儲存空間，包含擁有非揮發性以及高容量特性的整合型記憶體越來越被重視。由於結合了非揮發性記憶體(NVM)與動態隨機存取記憶體(DRAM)，整合型記憶體非常適合於這些大量數據的應用。然而NVM往往含有一些缺點如較高的存取時間或較低的壽命。為了改善存取時間以及壽命的問題，我們提出了全域淨頁優先置換法(Global Clean Page First, GCPF)來減少對於NVM的寫入次數，進而提升壽命。接著，我們也提出了一個索引感知多重流向預存取方法(Index Aware Multi-Stream Prefetcher, IAMSP)，考量各別預存取資料的索引來更準確地從NVM中預存取資料，進而減少平均存取時間。在實驗中，我們利用了擁有大範圍記憶體腳蹤的測試資料來檢測我們提出的方法。實驗結果顯示與傳統LRU方法比較之下，GCPF平均提高約56.8%的壽命。當更進一步使用預存取方法在GCPF上時，壽命並不會被減少。此外，IAMSP可以減少約42.0%的DRAM資料錯失次數，相比之下，擁有動態改變預存取深度的近代預存取方法只能減少38.0%。結合了GCPF以及IAMSP，平均存取時間與LRU方法比較之下可以減少28.8%，並且應用這兩個方法時所花費的硬體成本也非常微小。

As cloud computing and big data applications become more popular, the demand for large capacity memory and preservation of data in memory increases. Therefore, non-volatile memory (NVM) with high capacity has been actively developed. A hybrid memory that consists of 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 shorter lifetime and higher latency. To improve the lifetime and latency issues of the 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) to consider the index of prefetch candidates individually 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 enhance lifetime by 56.8% compared to LRU on average. When applying prefetching schemes on GCPF, the lifetime is insignificantly degraded. In addition, IAMSP reduce 42.0% DRAM misses compared to LRU, while a modern prefetcher that can change the prefetch degree dynamically only reduces 38.0% DRAM misses on average. When applying both GCPF and IAMSP, the average access latency can be reduced by 28.8% compared to LRU, and the overall hardware overhead of the two schemes is insignificant.

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