HAP: Hybrid-memory-Aware Partition in shared Last-Level Cache

Data-center servers require large capacity main memory to run multiple workloads simultaneously. However, the scalability and power consumption of DRAM limit its capability of constructing large capacity memory. Emerging non-volatile memories (e.g. PCM and STT-RAM) provide better scalability and lower power leakage than DRAM. Especially, hybrid memory consisting of DRAM and NVM is able to exploit advantages of different memory medias. However, NVMs have a few drawbacks, such as relatively longer read and write latency. Cache miss at the shared last level cache (LLC) suffers from longer latency if the missing data resides in NVM. Current LLC policies manage the cache space without being aware of the underlying heterogeneous medias. This results in cache performance degradation if a large number of missing data come from NVM. Taking the asymmetric cache miss cost into account, we first propose a new performance metric -TMPKI, which can exactly reflect the LLC performance on the top of hybrid memories. Then we propose a hybrid memory aware cache partitioning technique (HAP) to dynamically adjust the cache spaces for DRAM and NVM data based on TMPKI. Experimental results show that HAP improves performance against the traditional LRU policy by up to 54.3% (19.6% on average) while it incurs a little storage overhead (0.2%).