A Contention-Friendly Methodology for Search Structures

In this paper, a new methodology for writing concurrent data structures is proposed. This methodology limits the high contention induced by today's mutlicore environments to come up with efficient alternatives to most widely used search structures, including skip lists, binary search trees and hash tables. Data structures are generally constrained to guarantee a big-oh step complexity even in the presence of concurrency. By contrast our methodology guarantees the big-oh complexity only in the absence of contention and limits the contention when concurrency appears. The key concept lies in dividing update operations within an eager abstract access that returns rapidly for efficiency reason and a lazy structural adaptation that may be postponed to diminish contention. We illustrate our methodology with three contention-friendly data structures: a lock based skip list and binary search tree, and a lock-free hash table. Our evaluation clearly shows that our contention-friendly data structures are more efficient than their non-contention-friendly counterparts. In particular, our lockbased skip list is up to 1:3 faster than the Java concurrent skip list, our lock-based tree is up to 2:2 faster than the most recent concurrent tree algorithm we are aware of, and our lock-free hash table outperforms by up to 1:2 the Java concurrent hash table. We also present contention-friendly versions of the skip list and binary search tree using transactional memory. Even though our transaction-based data structures are substantially slower than our lock-based ones, they inherit compositionality from transactional memory and outperform their non-contention-friendly counterparts by 1:5 on average.Ce rapport présente une approche méthodologique pour les structures de recherche concurrentes avec des applcations aux listes á saut (skip list), arbres et table de hachage (hash table)

Self Adjusting Contention Friendly Concurrent Binary Search Tree by Lazy Splaying

We present a partial blocking implementation of concurrent binary search tree data structure that is contention friendly, fast and scales well. It uses a technique, called lazy splaying to move frequently accessed items close to the root without making the root of the tree a sequential bottleneck. Most of the self adjusting binary search trees are constrained to guarantee the height of a tree even in the presence of concurrency. But, this methodology roughly guarantees the height of a tree only in the absence of contention and limits the contention during concurrent accesses. The main idea is to divide the update operation into two operations:an eager abstract modification with lazy splayingthat completes quickly and makes at most one local rotation of the tree on each access as a function of historical access frequencies; anda lazy structural adaptation with long/semi splayingwhich implements top down recursive splaying of the tree that may be postponed to diminish contention and re-balance the tree during less contention. This way, the frequently accessed items perform full splaying but after few accesses only and always appear near the root of the tree. Whereas, the infrequently accessed items will not get enough pushes up the tree and stay in the bottom part of the tree. As in sequential counting based splay tree, the amortized time bound of each operation isO(log N), whereNis the number of items in the tree

A Speculation-Friendly Binary Search Tree

We introduce the first binary search tree algorithm designed for speculative executions. Prior to this work, tree structures were mainly designed for their pessimistic (non-speculative) accesses to have a bounded complexity. Researchers tried to evaluate transactional memory using such tree structures whose prominent example is the red-black tree library developed by Oracle Labs that is part of multiple benchmark distributions. Although well-engineered, such structures remain badly suited for speculative accesses, whose step complexity might raise dramatically with contention. We show that our speculation-friendly tree outperforms the existing transaction-based version of the AVL and the red-black trees. Its key novelty stems from the decoupling of update operations: they are split into one transaction that modifies the abstraction state and multiple ones that restructure its tree implementation in the background. In particular, the speculation-friendly tree is shown correct, reusable and it speeds up a transaction-based travel reservation application by up to 3:5

A Concurrency-Optimal Binary Search Tree

The paper presents the first \emph{concurrency-optimal} implementation of a binary search tree (BST). The implementation, based on a standard sequential implementation of an internal tree, ensures that every \emph{schedule} is accepted, i.e., interleaving of steps of the sequential code, unless linearizability is violated. To ensure this property, we use a novel read-write locking scheme that protects tree \emph{edges} in addition to nodes. Our implementation outperforms the state-of-the art BSTs on most basic workloads, which suggests that optimizing the set of accepted schedules of the sequential code can be an adequate design principle for efficient concurrent data structures

DeltaTree: A Practical Locality-aware Concurrent Search Tree

As other fundamental programming abstractions in energy-efficient computing, search trees are expected to support both high parallelism and data locality. However, existing highly-concurrent search trees such as red-black trees and AVL trees do not consider data locality while existing locality-aware search trees such as those based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations. This paper presents DeltaTree, a practical locality-aware concurrent search tree that combines both locality-optimisation techniques from vEB-based trees and concurrency-optimisation techniques from non-blocking highly-concurrent search trees. DeltaTree is a $k$-ary leaf-oriented tree of DeltaNodes in which each DeltaNode is a size-fixed tree-container with the van Emde Boas layout. The expected memory transfer costs of DeltaTree's Search, Insert, and Delete operations are $O(\log_B N)$, where $N, B$ are the tree size and the unknown memory block size in the ideal cache model, respectively. DeltaTree's Search operation is wait-free, providing prioritised lanes for Search operations, the dominant operation in search trees. Its Insert and {\em Delete} operations are non-blocking to other Search, Insert, and Delete operations, but they may be occasionally blocked by maintenance operations that are sometimes triggered to keep DeltaTree in good shape. Our experimental evaluation using the latest implementation of AVL, red-black, and speculation friendly trees from the Synchrobench benchmark has shown that DeltaTree is up to 5 times faster than all of the three concurrent search trees for searching operations and up to 1.6 times faster for update operations when the update contention is not too high

Efficient Lock-free Binary Search Trees

In this paper we present a novel algorithm for concurrent lock-free internal binary search trees (BST) and implement a Set abstract data type (ADT) based on that. We show that in the presented lock-free BST algorithm the amortized step complexity of each set operation - {\sc Add}, {\sc Remove} and {\sc Contains} - is $O(H(n) + c)$, where, $H(n)$ is the height of BST with $n$ number of nodes and $c$ is the contention during the execution. Our algorithm adapts to contention measures according to read-write load. If the situation is read-heavy, the operations avoid helping pending concurrent {\sc Remove} operations during traversal, and, adapt to interval contention. However, for write-heavy situations we let an operation help pending {\sc Remove}, even though it is not obstructed, and so adapt to tighter point contention. It uses single-word compare-and-swap (\texttt{CAS}) operations. We show that our algorithm has improved disjoint-access-parallelism compared to similar existing algorithms. We prove that the presented algorithm is linearizable. To the best of our knowledge this is the first algorithm for any concurrent tree data structure in which the modify operations are performed with an additive term of contention measure.Comment: 15 pages, 3 figures, submitted to POD