Dynamic NUMA-Aware Data Structure Replication

Computing infrastructure is increasingly composed of non-uniform memory access (NUMA) architectures, such as machines with multiple sockets and rackscale systems with shared memory. A powerful technique to effectively use such architectures is to replicate data structures across NUMA domains, so threads can quickly access a local replica of data. Unfortunately, existing replication methods support only a static set of replicas, which is mismatched to the dynamic needs of modern systems. We introduce LD-NR, a replication technique that allows the number and location of replicas to change dynamically. LD-NR can adapt to workloads that change over time, such as applications that scale up and down across NUMA domains, whereas NR, the state of the art in NUMA-aware data structure replication, is limited to a static replica set. LD-NR achieves this benefit with negligible memory and performance overhead when configured identically to NR. We show that a hashmap dynamically replicated with LD-NR achieves 2.6 times better throughput than the best performing concurrent hashmap implementation, and provides 97% of the throughput provided by NR with an ideal, static configuration. We further demonstrate the utility of LD-NR to optimize complex systems by using it to enable a granular, per-process page table replication policy in a rackscale operating system.