We present a linearizable, non-blocking @math -ary search tree ( @math -ST) that supports fast searches and range queries. Our algorithm uses single-word compare-and-swap (CAS) operations, and tolerates any number of crash failures. Performance experiments show that, for workloads containing small range queries, our @math -ST significantly outperforms other algorithms which support these operations, and rivals the performance of a leading concurrent skip-list, which provides range queries that cannot always be linearized.
The second tree, Snap, is a lock-based AVL tree due to Bronson et al. @cite_10 . Whereas Ctrie added sequence numbers, Snap each node to indicate that it should no longer by modified. Updates are organized into , with each epoch represented by an object in memory containing a count of the number of active updates belonging to that epoch. A snapshot marks the root node, ends the current epoch, and blocks further updates from starting until all updates in the current epoch finish. Once updates are no longer blocked, they copy and mark each node they see whose parent is marked. Like Ctrie, this pushes work from snapshots onto subsequent updates. If these snapshots are used to compute small range queries, this may result in excessive duplication of unrelated parts of the structure.
@cite_10
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We propose a concurrent relaxed balance AVL tree algorithm that is fast, scales well, and tolerates contention. It is based on optimistic techniques adapted from software transactional memory, but takes advantage of specific knowledge of the the algorithm to reduce overheads and avoid unnecessary retries. We extend our algorithm with a fast linearizable clone operation, which can be used for consistent iteration of the tree. Experimental evidence shows that our algorithm outperforms a highly tuned concurrent skip list for many access patterns, with an average of 39 higher single-threaded throughput and 32 higher multi-threaded throughput over a range of contention levels and operation mixes.