Qing Wang, Youyou Lu, Junru Li, Minhui Xie, J. Shu
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引用次数: 4
摘要
我们提出了Nap,这是一种黑盒方法,它将并发持久内存(PM)索引转换为非统一内存访问(NUMA)感知的对应物。基于对现实工作负载总是具有倾斜访问模式的观察,Nap在现有并发PM索引的顶部引入了numa感知层(NAL),并将对热门项目的访问引导到该层。NAL在PM中维护(1)每个节点的部分视图,用于服务具有故障原子性的插入/更新/删除操作;(2)在DRAM中维护全局视图,用于服务查找操作。NAL消除了对热点项目的远程PM访问,而不会引起额外的本地PM访问。此外,为了处理动态工作负载,Nap采用了快速NAL切换机制。我们使用Nap转换五个最先进的PM索引。在使用Optane DC Persistent Memory的四节点机器上进行的评估表明,在写密集型和读密集型工作负载下,Nap可以分别将吞吐量提高2.3倍和1.56倍。
Nap: Persistent Memory Indexes for NUMA Architectures
We present Nap, a black-box approach that converts concurrent persistent memory (PM) indexes into non-uniform memory access (NUMA)-aware counterparts. Based on the observation that real-world workloads always feature skewed access patterns, Nap introduces a NUMA-aware layer (NAL) on the top of existing concurrent PM indexes, and steers accesses to hot items to this layer. The NAL maintains (1) per-node partial views in PM for serving insert/update/delete operations with failure atomicity and (2) a global view in DRAM for serving lookup operations. The NAL eliminates remote PM accesses to hot items without inducing extra local PM accesses. Moreover, to handle dynamic workloads, Nap adopts a fast NAL switch mechanism. We convert five state-of-the-art PM indexes using Nap. Evaluation on a four-node machine with Optane DC Persistent Memory shows that Nap can improve the throughput by up to 2.3× and 1.56× under write-intensive and read-intensive workloads, respectively.