Gengar: An RDMA-based Distributed Hybrid Memory Pool - Duan, Zhuohui; Liu, Haikun; Lu, Haodi; Liao, Xiaofei; Jin, Hai; Zhang, Yu; He, Bingsheng (Proceedings of the International Conference on Distributed Computing Systems, 2021)
Byte-addressable Non-volatile Memory (NVM) technologies promise higher density and lower cost than DRAM. They have been increasingly employed for data center applications. Despite many previous studies on using NVM in a single machine, there remain challenges to best utilize it in a distributed data center environment. This paper presents Gengar, an RDMA-enabled Distributed Shared Hybrid Memory (DSHM) pool with simple programming APIs on viewing remote NVM and DRAM in a global memory space. We propose to exploit semantics of RDMA primitives to identify frequently-accessed data in the hybrid memory pool, and cache it in distributed DRAM buffers. We redesign RDMA communication protocols to reduce the bottleneck of RDMA write latency by leveraging a proxy mechanism. Gengar also supports memory sharing among multiple users with data consistency guarantee. We evaluate Gengar in a real testbed equipped with Intel Optane DC Persistent DIMMs. Experimental results show that Gengar significantly improves the performance of public benchmarks such as MapReduce and YCSB by up to 70 % compared with state-of-the-art DSHM systems.