计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 130-135.doi: 10.11896/jsjkx.190800115
刘伟1,2,3, 孙童心1, 杜薇1,2
LIU Wei1,2,3, SUN Tong-xin1, DU Wei1,2
摘要: 大数据时代催生了很多以数据为中心的技术和应用,这对计算机主存的速度、容量、能耗提出了更高的要求。为了解决传统DRAM(Dynamic Random Access Memory)内存遇到的瓶颈,由DRAM和非易失性存储NVM(Non-Volatile Memory)组成的混合内存技术受到了广泛的关注。在混合内存环境下,缓存的性能至关重要。针对混合内存环境,已有的缓存替换策略研究都是对LRU2思想的改进,虽然考虑了DRAM数据和NVM数据缺失惩罚不对称的现象,但是在面对LRU(Least Recently Used)性能差的负载时也会存在缓存抖动和污染问题,仍然存在优化空间。文中针对不同类型的负载特点,考虑了不同访问模式下DRAM与NVM数据的竞争关系,提出了一种动态可调整的缓存替换策略DLRP(Dynamic Level Replacement Policy)。该策略在面对不同类型的负载时能动态地选择最优的替换策略,在保持整体命中率较好的同时降低了NVM的缺失和写回。实验结果表明,相比WBAR策略,DLRP不仅在IPC上有平均16.5%的提升,而且在能耗和写操作数量上分别降低了5.2%和5.1%。
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