GATLB: A Granularity-Aware TLB to Support Multi-Granularity Pages in Hybrid Memory System
Yujuan Tan1,2, Yujie Xie1, Zhulin Ma1, Zhichao Yan3, Zhichao Zhang1, Duo Liu1 and Xianzhang Chen1
1College of Computer Science, Chongqing University, Chongqing, China
2Wuhan National Laboratory for Optoelectronics, Wuhan, China
3HewlettPackard Enterprise, San Jose, USA
ABSTRACT
The parallel hybrid memory system that combines Non-volatile Memory (NVM) and DRAM can effectively expand the memory capacity. But it puts lots of pressure on TLB due to a limited TLB capacity. The superpage technology that manages pages with a large granularity (e.g., 2MB) is usually used to improve the TLB performance. However, its coarse-grained granularity conflicts with the fine-grained page migration in the hybrid memory system, resulting in serious invalid migration and page fragmentation problems. To solve these problems, we propose to maintain the coexistence of multi-granularity pages, and design a smart TLB called GATLB to support multigranularity page management, coalesce consecutive pages and adapt to various changes in page size. Compared with the existing TLB technologies, GATLB can not only perceive page granularity to effectively expand the TLB coverage and reduce miss rate, but also provide faster address translation with a much lower overhead. Our experimental evaluations show that GATLB can expand the TLB coverage by 7.09x, reduce the TLB miss rate by 91.1%, and shorten the address translation cycle by 49.41%.
Keywords: TLB, Multi-Granularity Pages, Parallel Hybrid Memory.
