Non-Blocking Doubly-Linked Lists with Good Amortized Complexity

We present a new non-blocking doubly-linked list implementation for an asynchronous shared-memory system. It is the first such implementation for which an upper bound on amortized time complexity has been proved. In our implementation, operations access the list via cursors. Each cursor is located at an item in the list and is local to a process. In our implementation, cursors can be used to traverse and update the list, even as concurrent operations modify the list. The implementation supports two update operations, insertBefore and delete, and two move operations, moveRight and moveLeft. An insertBefore(c, x) operation inserts an item x into the list immediately before the cursor c\u27s location. A delete(c) operation removes the item at the cursor c\u27s location and sets the cursor to the next item in the list. The move operations move the cursor one position to the right or left. Update operations use single-word Compare&Swap instructions. Move operations only read shared memory and never change the state of the data structure. If all update operations modify different parts of the list, they run completely concurrently. A cursor is active if it is initialized, but not yet removed from the process\u27s set of cursors. Let c.(op) be the maximum number of active cursors at any one time during the operation op. The amortized step complexity is O(c.(op)) for each update op and O(1) for each move. We provide a detailed correctness proof and amortized analysis of our implementation

The SkipTrie: low-depth concurrent search without rebalancing

To date, all concurrent search structures that can support predecessor queries have had depth logarithmic in m, the number of elements. This paper introduces the SkipTrie, a new concurrent search structure supporting predecessor queries in amortized expected O(log log u + c) steps, insertions and deletions in O(c log log u), and using O(m) space, where u is the size of the key space and c is the contention during the recent past. The SkipTrie is a probabilistically-balanced version of a y-fast trie consisting of a very shallow skiplist from which randomly chosen elements are inserted into a hash-table based x-fast trie. By inserting keys into the x-fast-trie probabilistically, we eliminate the need for rebalancing, and can provide a lock-free linearizable implementation. To the best of our knowledge, our proof of the amortized expected performance of the SkipTrie is the first such proof for a tree-based data structure.National Science Foundation (U.S.) (Grant CCF-1217921)United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio

The Amortized Analysis of a Non-blocking Chromatic Tree

A non-blocking chromatic tree is a type of balanced binary search tree where multiple processes can concurrently perform search and update operations. We prove that a certain implementation has amortized cost O(dot{c} + log n) for each operation, where dot{c} is the maximum number of concurrent operations at any point during the execution and n is the maximum number of keys in the tree during the operation. This amortized analysis presents new challenges compared to existing analyses of other non-blocking data structures

Non-blocking Priority Queue based on Skiplists with Relaxed Semantics

Priority queues are data structures that store information in an orderly fashion. They are of tremendous importance because they are an integral part of many applications, like Dijkstra’s shortest path algorithm, MST algorithms, priority schedulers, and so on. Since priority queues by nature have high contention on the delete_min operation, the design of an efficient priority queue should involve an intelligent choice of the data structure as well as relaxation bounds on the data structure. Lock-free data structures provide higher scalability as well as progress guarantee than a lock-based data structure. That is another factor to be considered in the priority queue design. We present a relaxed non-blocking priority queue based on skiplists. We address all the design issues mentioned above in our priority queue. Use of skiplists allows multiple threads to concurrently access different parts of the skiplist quickly, whereas relaxing the priority queue delete_min operation distributes contention over the skiplist instead of just at the front. Furthermore, a non-blocking implementation guarantees that the system will make progress even when some process fails. Our priority queue is internally composed of several priority queues, one for each thread and one shared priority queue common to all threads. Each thread selects the best value from its local priority queue and the shared priority queue and returns the value. In case a thread is unable to delete an item, it tries to spy items from other threads\u27 local priority queues. We experimentally and theoretically show the correctness of our data structure. We also compare the performance of our data structure with other variations like priority queues based on coarse-grained skiplists for both relaxed and non-relaxed semantics

Parallel Finger Search Structures

In this paper we present two versions of a parallel finger structure FS on p processors that supports searches, insertions and deletions, and has a finger at each end. This is to our knowledge the first implementation of a parallel search structure that is work-optimal with respect to the finger bound and yet has very good parallelism (within a factor of O(log p)^2) of optimal). We utilize an extended implicit batching framework that transparently facilitates the use of FS by any parallel program P that is modelled by a dynamically generated DAG D where each node is either a unit-time instruction or a call to FS. The work done by FS is bounded by the finger bound F_L (for some linearization L of D), i.e. each operation on an item with distance r from a finger takes O(log r+1) amortized work. Running P using the simpler version takes O((T_1+F_L)/p + T_infty + d * ((log p)^2 + log n)) time on a greedy scheduler, where T_1, T_infty are the size and span of D respectively, and n is the maximum number of items in FS, and d is the maximum number of calls to FS along any path in D. Using the faster version, this is reduced to O((T_1+F_L)/p + T_infty + d *(log p)^2 + s_L) time, where s_L is the weighted span of D where each call to FS is weighted by its cost according to F_L. FS can be extended to a fixed number of movable fingers. The data structures in our paper fit into the dynamic multithreading paradigm, and their performance bounds are directly composable with other data structures given in the same paradigm. Also, the results can be translated to practical implementations using work-stealing schedulers