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per-Operation Reusability based Allocation and Migration Policy for Hybrid Cache
IEEE Transactions on Computers ( IF 3.6 ) Pub Date : 2020-02-01 , DOI: 10.1109/tc.2019.2944163
Minsik Oh , Kwangsu Kim , Duheon Choi , Hyuk-Jun Lee , Eui-Young Chung

Recently, a hybrid cache consisting of SRAM and STT-RAM has attracted much attention as a future memory by complementing each other with different memory characteristics. Prior works focused on developing data allocation and migration techniques considering write-intensity to reduce write energy at STT-RAM. However, these works often neglect the impact of operation-specific reusability of a cache line. In this paper, we propose an energy-efficient per-operation reusability-based allocation and migration policy (ORAM) with a unified LRU replacement policy. First, to select an adequate memory type for allocation, we propose a cost function based on per-operation reusability – gain from an allocated cache line and loss from an evicted cache line for different memory types – which exploits the temporal locality. Besides, we present a migration policy, victim and target cache line selection scheme, to resolve memory type inconsistency between replacement policy and the allocation policy, with further energy reduction. Experiment results show an average energy reduction in the LLC and the main memory by 12.3 and 21.2 percent, and the improvement of latency and execution time by 21.2 and 8.8 percent, respectively, compared with a baseline hybrid cache management. In addition, the Energy-Delay Product (EDP) is improved by 36.9 percent over the baseline.

中文翻译:

基于每个操作可重用性的混合缓存分配和迁移策略

最近,由 SRAM 和 STT-RAM 组成的混合缓存作为未来的存储器通过具有不同存储器特性的相互补充而备受关注。之前的工作重点是开发考虑写入强度的数据分配和迁移技术,以减少 STT-RAM 的写入能量。然而,这些工作往往忽略了缓存线特定于操作的可重用性的影响。在本文中,我们提出了一种具有统一 LRU 替换策略的、节能的、基于每操作可重用性的分配和迁移策略(ORAM)。首先,为了选择合适的内存类型进行分配,我们提出了一个基于每个操作可重用性的成本函数——从分配的缓存线中获得收益,从不同内存类型的逐出缓存线中获得损失——它利用了时间局部性。此外,我们提出了移民政策,受害者和目标缓存线选择方案,解决替换策略和分配策略之间的内存类型不一致,进一步降低能量。实验结果表明,与基准混合缓存管理相比,LLC 和主内存的平均能量降低了 12.3% 和 21.2%,延迟和执行时间分别提高了 21.2% 和 8.8%。此外,能量延迟积 (EDP) 比基线提高了 36.9%。分别与基准混合缓存管理进行比较。此外,能量延迟积 (EDP) 比基线提高了 36.9%。分别与基准混合缓存管理进行比较。此外,能量延迟积 (EDP) 比基线提高了 36.9%。
更新日期:2020-02-01
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