An adaptive concurrent priority queue for NUMA architectures

Designing scalable concurrent priority queues for contemporary NUMA servers is challenging. Several NUMA-unaware implementations can scale up to a high number of threads exploiting the potential parallelism of the insert operations. In contrast, in deleteMin-dominated workloads, threads compete for accessing the same memory locations, i.e. the first item in the priority queue. In such cases, NUMA-aware implementations are typically used, since they reduce the coherence traffic between the nodes of a NUMA system. In this work, we propose an adaptive priority queue, called SmartPQ, that tunes itself by automatically switching between NUMA-unaware and NUMA-aware algorithmic modes to provide the highest available performance under all workloads. SmartPQ is built on top of NUMA Node Delegation (Nuddle), a low overhead technique to construct NUMA-aware data structures using any arbitrary NUMA-unaware implementation as its backbone. Moreover, SmartPQ employs machine learning to decide when to switch between its two algorithmic modes. As our evaluation reveals, it achieves the highest available performance with 88% success rate and dynamically adapts between a NUMA-aware and a NUMA-unaware mode, without overheads, while performing up to 1.83 times better performance than Spraylist, the state-of-the-art NUMA-unaware priority queue.