Improving efficacy of internal binary search trees using local recovery

Abstract

Binary Search Tree (BST) is an important data structure for managing ordered data. Many algorithms-blocking as well as nonblocking-have been proposed for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations based on both external and internal representations of a search tree.An important step in executing an operation on a tree is to traverse the tree from top-to-down in order to locate the operation's window. A process may need to perform this traversal several times to handle any failures occurring due to other processes performing conflicting actions on the tree. Most concurrent algorithms that have proposed so far use a naïve approach and simply restart the traversal from the root of the tree.In this work, we present a new approach to recover from such failures more efficiently in a concurrent binary search tree based on internal representation using local recovery by restarting the traversal from the "middle" of the tree in order to locate an operation's window. Our approach is sufficiently general in the sense that it can be applied to a variety of concurrent binary search trees based on both blocking and non-blocking approaches.Using experimental evaluation, we demonstrate that our local recovery approach can yield significant speed-ups of up to 69% for many concurrent algorithms.

Improving efficacy of internal binary search trees using local recovery

“…Second, to identify a restart point, the algorithm examines the traversal log to find the latest node in the log that is unmarked and safe. For search and delete operations, further optimizations are possible that avoid the need to restart traversal during the seek phase [5].…”

“…We see smaller improvements for CASTLE and CITRUS as they are lock-based algorithms with no helping performed during tree traversal. More details of the experimental evaluation can be found in [5].…”