Effect of portable fine-grained locality on energy efficiency and performance in concurrent search trees

Recent research has suggested that improving fine-grained data-locality is one of the main approaches to improving energy efficiency and performance. However, no previous research has investigated the effect of the approach on these metrices in the case of concurrent data structures. This paper investigates how fine-grained data locality influences energy efficiency and performance in concurrent search trees, a crucial data structure that is widely used in several important systems. We conduct a set of experiments on three lock-based concurrent search trees: DeltaTree, a portable fine-grained locality-aware concurrent search tree; CBTree, a coarse-grained locality-aware B+tree; and BST-TK, a locality-oblivious concurrent search tree. We run the experiments on a commodity x86 platform and an embedded ARM platform. The experimental results show that DeltaTree has 13--25% better energy efficiency and 10--22% more operations/second on the x86 and ARM platforms, respectively. The results confirm that portable fine-grained locality can improve energy efficiency and performance in concurrent search trees.