Skr-Tree: A Highly Responsive B-Tree on General Skyrmion Racetrack Memories

Abstract

Skyrmion racetrack memory (SK-RM) has become a promising memory medium in future computer systems, thanks to the exceptional access performance and storage density. Its unique features, such as the supports of parallel reads/writes and random inserts/deletes, create new opportunities to address the design challenges of existing data structures. In particular, directly storing a B-tree on SK-RM might result in occasional spikes in the latency when inserting a data item into the B-tree. The primary cause of the unstable performance is due to the inefficient node splitting procedure, in which nearly half of the data items of the overflowing node must be migrated to a new node. In this work, we present Skr-tree, a highly responsive variant of B-tree for general SK-RMs. To reduce the worst-case latency of data item insertion, Skr-tree comes with a novel node splitting strategy called remapping split, which modifies the node metadata to remap a part of the data items of an overflowing node to a new node. To accompany the remapping split strategy, Skr-tree is also equipped with a node rebalancing strategy which amortizes the data item migration to different data item insert operations and reduces the instantaneous latency of node splitting. To validate the performance benefits of Skr-tree, a series of analytical and experimental studies have been performed, and the results are quite encouraging.